Source reconstruction
Mathilde Marie Duville
Notice: please, run the codes for figures in the command window to output them with a correct scale for visualization
General concepts
- The head model describes how the neuronal activity propagates through the skull and scalp. It reflects the geometrical (mesh) and conductive properties of the head.
- The source model describes the position of the sources that generate the activity. It is a matrix of size m neural sources * k samples (or y*x*z*k).
- The positions of the sensors must be aligned with both the head and the source models.
Forward modelling (i.e., how activity of the sources propagate into the sensors) can be computed from those three components. From the forward model, the leadfield matrix of size n sensors*x*y*z (or n*m neural sources) at each time sample, that describes the inverse problem can be solved. That is, the estimation of the sources' activity that correspond to the data measured by the sensors.
Note that the description of the head and source models, and of the position of the sensors must be expressed in the same coordinate system and with the same units.
As there is an infinite numbers of possibilities for sources' activity to generate the observed activity at the sensor level, some constraints must be established. For instance, the eLoreta method forces the activity of neighbouring sources to be correlated, whereas beameforming like linearly constrained minimum variance (LCMV) forces neighbouring sources to be uncorrelated. Also the model could force to express the activity of a unique and optimal single dipole (single dipole method).
Finally, the Independent component analysis (ICA) recovers a version of the original sources by multiplying the data by an unmixing matrix. It minimizes the mutual information between two sources by maximizing the individual entropies. ICA whitens (or sphere) the data, that is it removes any correlation within the data (e.g., between the activity recorded at various sensors). To do so, ICA "rotates" the data by applying a linear change of coordinate so that the variance on all axis are equal (each axis is a channel), and the covariance is null: signals are independent. The goal is to minimize the Gaussian distribution on all axis. A drawback is that perfect Gaussian sources could not be separated by ICA. Also, ICA is optimal only if they sources statistically independent. However, even when the sources are dependent, the algorithm finds a space where they are maximally independents (minimizes the mutual information). Finally, ICA can only separate linearly mixed sources.
Please see the PowerPoint slide dedicated to it for details about this method (TheoreticalExplanations.pdf).
Using Independent component analysis (ICA)
clear; clc; close all;
addpath(PathtoEEGLab);
[ALLEEG, EEG, CURRENTSET, ALLCOM] = eeglab;
Some menus items hidden. Use Preference menu to show them all.
eeglab: options file is C:\Users\mathi\eeg_options.m
Retrieving plugin versions from server...
Retreiving download statistics...
EEGLAB: adding "AMICA" v1.6.1 (see >> help eegplugin_amica) - new version 1.7 available
EEGLAB: adding "Biosig" to the path; subfolders (if any) might be missing from the path
EEGLAB: adding "Cleanline" v2.00 (see >> help eegplugin_cleanline)
EEGLAB: adding "Fieldtrip-lite" to the path; subfolders (if any) might be missing from the path
EEGLAB: adding "ICLabel" v1.2.6 (see >> help eegplugin_iclabel) - new version 1.6 available
EEGLAB: adding "MARA" v1.2 (see >> help eegplugin_MARA)
EEGLAB: adding "PrepPipeline" v0.55.4 (see >> help eegplugin_prepPipeline) - new version 0.56.0 available
EEGLAB: adding "clean_rawdata" v2.4 (see >> help eegplugin_clean_rawdata) - new version 2.91 available
EEGLAB: adding "dipfit" v4.2 (see >> help eegplugin_dipfit) - new version 5.4 available
Warning: Function maxNumCompThreads has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict.
EEGLAB: adding "dsi-master" v1.0.0 (see >> help eegplugin_dsi)
EEGLAB: adding "firfilt" v2.4 (see >> help eegplugin_firfilt) - new version 2.8 available
Warning: Function maxNumCompThreads has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict.
EEGLAB: adding "headModel-master" v1.0.2 (see >> help eegplugin_headModel)
EEGLAB: adding "headModel" v1.0.0 (see >> help eegplugin_headModel)
EEGLAB: adding "iirfilt" v1.03 (see >> help eegplugin_iirfilt) - new version 1.05 available
EEGLAB: adding "postAmicaUtility" v2.1 (see >> help eegplugin_postAmicaUtility)
Using SimBSI installed in: C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\simbsi
EEGLAB: adding "simbsi" v1.0.1 (see >> help eegplugin_simbsi)
% Here, an open data from EEGLab tutorial will be used
EEG = pop_loadset('filename','eeglab_dipole.set','filepath',...
'C:\\Users\\mathi\\OneDrive\\Documents\\Job\\GitHub\\MATLAB\\Codes\\SourceLocalization\\');
pop_loadset(): loading file C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Codes\SourceLocalization\eeglab_dipole.set ...
[ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, CURRENTSET);
Creating a new ALLEEG dataset 1
eeglab redraw
Note that the ICA decomposition must have been run beforehand, and the channel locations must be available. Please refer to the pre-processing (Preprocessing.mlx and Preprocessing.html).
Construct the head model. Here the template boundary element model (BEM) from the Montreal Neurological Institute (MNI) is used. A custom head model may be used: see the next sections of this document.
EEG = pop_dipfit_settings( EEG, 'hdmfile','C:\\Users\\mathi\\OneDrive\\Documents\\Job\\GitHub\\MATLAB\\Archives\\eeglab2021.1\\plugins\\dipfit\\standard_BEM\\standard_vol.mat',...
'coordformat','MNI','mrifile','C:\\Users\\mathi\\OneDrive\\Documents\\Job\\GitHub\\MATLAB\\Archives\\eeglab2021.1\\plugins\\dipfit\\standard_BEM\\standard_mri.mat',...
'chanfile','C:\\Users\\mathi\\OneDrive\\Documents\\Job\\GitHub\\MATLAB\\Archives\\eeglab2021.1\\plugins\\dipfit\\standard_BEM\\elec\\standard_1005.elc',...
'coord_transform',[0 0 0 0 0 -1.5708 1 1 1] ,'chansel',[1:69]);
[ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, CURRENTSET);
Create equivalent dipole models:
- Apply grid scanning: scann all possible positions (in the x*y*z model) to find a starting point to fit equivalent dipoles to every component. At this step some components may be assigned to a sipole of same coordinates.
- Apply non-linear interactive fitting: find the best positions for every equivalent dipole.
- Scan for the best-fitting dipole locations
% The first input are the component to scann. The second to fourth inputs
% are respectuvely the "grid in X-direction", "grid in Y-direction", and
% "grid in Z-direction". The size and spacing of the coarse grid can be
% specified (here, linspace(-85,85,24): 24 steps equally spaced between
% -radius and +radius of the sphere that best matches the electrode positions).
% The last input is a rejection threshold: maximum residual variance
% accepted. components that do not resemble a dipolar field distribution
% will not be assigned a dipole location (here, threshold: 40%).
EEG_coarse_scan = pop_dipfit_gridsearch(EEG, [1:71] ,...
[-85 -77.6087 -70.2174 -62.8261 -55.4348 -48.0435 ...
-40.6522 -33.2609 -25.8696 -18.4783 -11.087 -3.69565 ...
3.69565 11.087 18.4783 25.8696 33.2609 40.6522...
48.0435 55.4348 62.8261 70.2174 77.6087 85] ,...
[-85 -77.6087 -70.2174 -62.8261 -55.4348 -48.0435 ...
-40.6522 -33.2609 -25.8696 -18.4783 -11.087 -3.69565 ...
3.69565 11.087 18.4783 25.8696 33.2609 40.6522 ...
48.0435 55.4348 62.8261 70.2174 77.6087 85] ,...
[0 7.72727 15.4545 23.1818 30.9091 38.6364 ...
46.3636 54.0909 61.8182 69.5455 77.2727 85] ,0.4);
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 71 topographies
Warning: the EEG data is not average referenced, correcting this
computing the leadfields for the gridsearch on the fly
creating sourcemodel based on user specified 3D grid
using electrodes specified in the configuration
2900 dipoles inside, 4012 dipoles outside brain
making tight grid
2900 dipoles inside, 2380 dipoles outside brain
the call to "ft_prepare_sourcemodel" took 1 seconds
scanning grid
scanning grid location 2900/2900
found minimum after scanning for topography 1 on grid point [-40.6522 -55.4348 0]
found minimum after scanning for topography 2 on grid point [-3.69565 55.4348 0]
found minimum after scanning for topography 3 on grid point [3.69565 -70.2174 15.4545]
found minimum after scanning for topography 4 on grid point [-48.0435 -3.69565 0]
found minimum after scanning for topography 5 on grid point [-48.0435 -85 0]
found minimum after scanning for topography 6 on grid point [11.087 -40.6522 46.3636]
found minimum after scanning for topography 7 on grid point [40.6522 -18.4783 0]
found minimum after scanning for topography 8 on grid point [25.8696 -62.8261 0]
found minimum after scanning for topography 9 on grid point [-33.2609 -85 7.72727]
found minimum after scanning for topography 10 on grid point [25.8696 25.8696 38.6364]
found minimum after scanning for topography 11 on grid point [-11.087 -85 30.9091]
found minimum after scanning for topography 12 on grid point [-11.087 18.4783 30.9091]
found minimum after scanning for topography 13 on grid point [-3.69565 40.6522 0]
found minimum after scanning for topography 14 on grid point [18.4783 -3.69565 46.3636]
found minimum after scanning for topography 15 on grid point [3.69565 -48.0435 15.4545]
found minimum after scanning for topography 16 on grid point [11.087 3.69565 69.5455]
found minimum after scanning for topography 17 on grid point [-25.8696 -33.2609 54.0909]
found minimum after scanning for topography 18 on grid point [3.69565 33.2609 30.9091]
found minimum after scanning for topography 19 on grid point [48.0435 18.4783 38.6364]
found minimum after scanning for topography 20 on grid point [48.0435 -18.4783 38.6364]
found minimum after scanning for topography 21 on grid point [3.69565 55.4348 0]
found minimum after scanning for topography 22 on grid point [3.69565 3.69565 23.1818]
found minimum after scanning for topography 23 on grid point [-33.2609 18.4783 23.1818]
found minimum after scanning for topography 24 on grid point [-33.2609 -55.4348 15.4545]
found minimum after scanning for topography 25 on grid point [25.8696 -70.2174 0]
found minimum after scanning for topography 26 on grid point [62.8261 18.4783 7.72727]
found minimum after scanning for topography 27 on grid point [62.8261 -33.2609 0]
found minimum after scanning for topography 28 on grid point [18.4783 -62.8261 0]
found minimum after scanning for topography 29 on grid point [-40.6522 -33.2609 54.0909]
found minimum after scanning for topography 30 on grid point [-48.0435 -85 0]
found minimum after scanning for topography 31 on grid point [-55.4348 18.4783 0]
found minimum after scanning for topography 32 on grid point [40.6522 -11.087 54.0909]
found minimum after scanning for topography 33 on grid point [48.0435 55.4348 0]
found minimum after scanning for topography 34 on grid point [48.0435 -55.4348 38.6364]
found minimum after scanning for topography 35 on grid point [-55.4348 -40.6522 38.6364]
found minimum after scanning for topography 36 on grid point [-11.087 -11.087 38.6364]
found minimum after scanning for topography 37 on grid point [-11.087 -11.087 30.9091]
found minimum after scanning for topography 38 on grid point [70.2174 -40.6522 0]
found minimum after scanning for topography 39 on grid point [-18.4783 3.69565 54.0909]
found minimum after scanning for topography 40 on grid point [70.2174 -18.4783 0]
found minimum after scanning for topography 41 on grid point [-62.8261 -40.6522 0]
found minimum after scanning for topography 42 on grid point [40.6522 25.8696 23.1818]
found minimum after scanning for topography 43 on grid point [-55.4348 33.2609 23.1818]
found minimum after scanning for topography 44 on grid point [-70.2174 -18.4783 0]
found minimum after scanning for topography 45 on grid point [3.69565 -70.2174 61.8182]
found minimum after scanning for topography 46 on grid point [-40.6522 -85 0]
found minimum after scanning for topography 47 on grid point [-62.8261 -18.4783 30.9091]
found minimum after scanning for topography 48 on grid point [-33.2609 62.8261 15.4545]
found minimum after scanning for topography 49 on grid point [-48.0435 48.0435 23.1818]
found minimum after scanning for topography 50 on grid point [-70.2174 -48.0435 0]
found minimum after scanning for topography 51 on grid point [55.4348 40.6522 0]
found minimum after scanning for topography 52 on grid point [33.2609 33.2609 54.0909]
found minimum after scanning for topography 53 on grid point [-25.8696 70.2174 0]
found minimum after scanning for topography 54 on grid point [-48.0435 55.4348 0]
found minimum after scanning for topography 55 on grid point [62.8261 11.087 0]
found minimum after scanning for topography 56 on grid point [55.4348 40.6522 15.4545]
found minimum after scanning for topography 57 on grid point [-3.69565 3.69565 38.6364]
found minimum after scanning for topography 58 on grid point [3.69565 -70.2174 61.8182]
found minimum after scanning for topography 59 on grid point [-25.8696 55.4348 0]
found minimum after scanning for topography 60 on grid point [-3.69565 -48.0435 61.8182]
found minimum after scanning for topography 61 on grid point [-33.2609 -11.087 69.5455]
found minimum after scanning for topography 62 on grid point [62.8261 -55.4348 0]
found minimum after scanning for topography 63 on grid point [11.087 25.8696 23.1818]
found minimum after scanning for topography 64 on grid point [25.8696 70.2174 0]
found minimum after scanning for topography 65 on grid point [25.8696 70.2174 0]
found minimum after scanning for topography 66 on grid point [-48.0435 48.0435 0]
found minimum after scanning for topography 67 on grid point [-11.087 -48.0435 61.8182]
found minimum after scanning for topography 68 on grid point [70.2174 -33.2609 15.4545]
found minimum after scanning for topography 69 on grid point [-62.8261 11.087 30.9091]
found minimum after scanning for topography 70 on grid point [70.2174 -3.69565 15.4545]
found minimum after scanning for topography 71 on grid point [40.6522 -70.2174 54.0909]
the call to "ft_dipolefitting" took 7 seconds
Fail to look up brain areas
Done
% [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, CURRENTSET);
% plot
pop_topoplot(EEG_coarse_scan, 0, [1:71] ,'Components scalp maps (2D) - Residual variances are specified in brackets',...
[8 9] ,1,'electrodes','off');
Plotting...
2. Apply non-linear interactive fitting: find the best positions for every equivalent dipole.
Here, an automated fitting is applied (and includes coarse fitting, so the previous step may be ignored). The algorithm uses non-linear optimization to find the exact dipole location.
EEG = pop_multifit(EEG, [1:71],'threshold',100,'dipplot','on','plotopt',{'normlen','on'});
Scanning dipolar grid to find acceptable starting positions...
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 71 topographies
Warning: the EEG data is not average referenced, correcting this
computing the leadfields for the gridsearch on the fly
creating sourcemodel based on user specified 3D grid
using electrodes specified in the configuration
2900 dipoles inside, 4012 dipoles outside brain
making tight grid
2900 dipoles inside, 2380 dipoles outside brain
the call to "ft_prepare_sourcemodel" took 0 seconds
scanning grid
scanning grid location 2900/2900
found minimum after scanning for topography 1 on grid point [-40.6522 -55.4348 0]
found minimum after scanning for topography 2 on grid point [-3.69565 55.4348 0]
found minimum after scanning for topography 3 on grid point [3.69565 -70.2174 15.4545]
found minimum after scanning for topography 4 on grid point [-48.0435 -3.69565 0]
found minimum after scanning for topography 5 on grid point [-48.0435 -85 0]
found minimum after scanning for topography 6 on grid point [11.087 -40.6522 46.3636]
found minimum after scanning for topography 7 on grid point [40.6522 -18.4783 0]
found minimum after scanning for topography 8 on grid point [25.8696 -62.8261 0]
found minimum after scanning for topography 9 on grid point [-33.2609 -85 7.72727]
found minimum after scanning for topography 10 on grid point [25.8696 25.8696 38.6364]
found minimum after scanning for topography 11 on grid point [-11.087 -85 30.9091]
found minimum after scanning for topography 12 on grid point [-11.087 18.4783 30.9091]
found minimum after scanning for topography 13 on grid point [-3.69565 40.6522 0]
found minimum after scanning for topography 14 on grid point [18.4783 -3.69565 46.3636]
found minimum after scanning for topography 15 on grid point [3.69565 -48.0435 15.4545]
found minimum after scanning for topography 16 on grid point [11.087 3.69565 69.5455]
found minimum after scanning for topography 17 on grid point [-25.8696 -33.2609 54.0909]
found minimum after scanning for topography 18 on grid point [3.69565 33.2609 30.9091]
found minimum after scanning for topography 19 on grid point [48.0435 18.4783 38.6364]
found minimum after scanning for topography 20 on grid point [48.0435 -18.4783 38.6364]
found minimum after scanning for topography 21 on grid point [3.69565 55.4348 0]
found minimum after scanning for topography 22 on grid point [3.69565 3.69565 23.1818]
found minimum after scanning for topography 23 on grid point [-33.2609 18.4783 23.1818]
found minimum after scanning for topography 24 on grid point [-33.2609 -55.4348 15.4545]
found minimum after scanning for topography 25 on grid point [25.8696 -70.2174 0]
found minimum after scanning for topography 26 on grid point [62.8261 18.4783 7.72727]
found minimum after scanning for topography 27 on grid point [62.8261 -33.2609 0]
found minimum after scanning for topography 28 on grid point [18.4783 -62.8261 0]
found minimum after scanning for topography 29 on grid point [-40.6522 -33.2609 54.0909]
found minimum after scanning for topography 30 on grid point [-48.0435 -85 0]
found minimum after scanning for topography 31 on grid point [-55.4348 18.4783 0]
found minimum after scanning for topography 32 on grid point [40.6522 -11.087 54.0909]
found minimum after scanning for topography 33 on grid point [48.0435 55.4348 0]
found minimum after scanning for topography 34 on grid point [48.0435 -55.4348 38.6364]
found minimum after scanning for topography 35 on grid point [-55.4348 -40.6522 38.6364]
found minimum after scanning for topography 36 on grid point [-11.087 -11.087 38.6364]
found minimum after scanning for topography 37 on grid point [-11.087 -11.087 30.9091]
found minimum after scanning for topography 38 on grid point [70.2174 -40.6522 0]
found minimum after scanning for topography 39 on grid point [-18.4783 3.69565 54.0909]
found minimum after scanning for topography 40 on grid point [70.2174 -18.4783 0]
found minimum after scanning for topography 41 on grid point [-62.8261 -40.6522 0]
found minimum after scanning for topography 42 on grid point [40.6522 25.8696 23.1818]
found minimum after scanning for topography 43 on grid point [-55.4348 33.2609 23.1818]
found minimum after scanning for topography 44 on grid point [-70.2174 -18.4783 0]
found minimum after scanning for topography 45 on grid point [3.69565 -70.2174 61.8182]
found minimum after scanning for topography 46 on grid point [-40.6522 -85 0]
found minimum after scanning for topography 47 on grid point [-62.8261 -18.4783 30.9091]
found minimum after scanning for topography 48 on grid point [-33.2609 62.8261 15.4545]
found minimum after scanning for topography 49 on grid point [-48.0435 48.0435 23.1818]
found minimum after scanning for topography 50 on grid point [-70.2174 -48.0435 0]
found minimum after scanning for topography 51 on grid point [55.4348 40.6522 0]
found minimum after scanning for topography 52 on grid point [33.2609 33.2609 54.0909]
found minimum after scanning for topography 53 on grid point [-25.8696 70.2174 0]
found minimum after scanning for topography 54 on grid point [-48.0435 55.4348 0]
found minimum after scanning for topography 55 on grid point [62.8261 11.087 0]
found minimum after scanning for topography 56 on grid point [55.4348 40.6522 15.4545]
found minimum after scanning for topography 57 on grid point [-3.69565 3.69565 38.6364]
found minimum after scanning for topography 58 on grid point [3.69565 -70.2174 61.8182]
found minimum after scanning for topography 59 on grid point [-25.8696 55.4348 0]
found minimum after scanning for topography 60 on grid point [-3.69565 -48.0435 61.8182]
found minimum after scanning for topography 61 on grid point [-33.2609 -11.087 69.5455]
found minimum after scanning for topography 62 on grid point [62.8261 -55.4348 0]
found minimum after scanning for topography 63 on grid point [11.087 25.8696 23.1818]
found minimum after scanning for topography 64 on grid point [25.8696 70.2174 0]
found minimum after scanning for topography 65 on grid point [25.8696 70.2174 0]
found minimum after scanning for topography 66 on grid point [-48.0435 48.0435 0]
found minimum after scanning for topography 67 on grid point [-11.087 -48.0435 61.8182]
found minimum after scanning for topography 68 on grid point [70.2174 -33.2609 15.4545]
found minimum after scanning for topography 69 on grid point [-62.8261 11.087 30.9091]
found minimum after scanning for topography 70 on grid point [70.2174 -3.69565 15.4545]
found minimum after scanning for topography 71 on grid point [40.6522 -70.2174 54.0909]
the call to "ft_dipolefitting" took 5 seconds
Fail to look up brain areas
Done
Scanning terminated. Refining dipole locations...
Searching dipoles locations...
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.133217 0.00292
1 24 0.0815365 7381 0.00185
2 32 0.0426433 0.285696 0.00108
3 60 0.0403563 1.00325 0.000483
4 76 0.0403531 0.0021829 0.00295
5 92 0.0401739 0.00113435 0.00576
6 100 0.0400685 0.1 0.00974
7 104 0.0400278 1 0.00639
8 108 0.0399657 1 0.00394
9 112 0.0397598 1 0.00433
10 124 0.0392396 0.410977 0.00293
11 136 0.0360621 0.987431 0.00044
12 148 0.0360618 0.0190913 0.000497
13 152 0.0360188 1 0.000513
14 156 0.0359943 1 0.000665
15 160 0.0359706 1 0.000354
16 164 0.0359619 1 0.00031
17 168 0.0359569 1 0.000316
18 172 0.0359557 1 0.000258
19 176 0.0359554 1 0.000248
First-order
Iteration Func-count f(x) Step-size optimality
20 180 0.0359551 1 0.000241
21 184 0.0359544 1 0.000242
22 188 0.0359528 1 0.000244
23 192 0.0359486 1 0.000246
24 196 0.0359388 1 0.000245
25 200 0.0359177 1 0.000258
26 204 0.0358789 1 0.000285
27 208 0.0358263 1 0.000251
28 212 0.0357776 1 0.000247
29 216 0.0357621 1 0.000261
30 220 0.0357568 1 7.76e-05
31 228 0.0357523 0.440016 7.52e-05
32 232 0.0357494 1 0.00012
33 236 0.0357486 1 5.14e-05
34 240 0.0357485 1 3.09e-06
35 244 0.0357485 1 4.37e-07
36 248 0.0357485 1 1.95e-07
37 252 0.0357485 1 1.87e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-52.5537 -75.624 -49.4989]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.101541 0.00224
1 20 0.0972285 820 0.00205
2 28 0.0797225 0.5 0.00103
3 36 0.0752041 0.5 0.000508
4 52 0.0739781 0.166249 0.00106
5 64 0.0738222 0.05 0.00251
6 72 0.0725704 0.5 0.00663
7 84 0.0720888 0.05 0.00731
8 92 0.071962 0.1 0.00355
9 100 0.0717433 0.5 0.00281
10 108 0.0717051 0.1 0.00717
11 112 0.0716374 1 0.00744
12 116 0.0715395 1 0.00658
13 120 0.0711481 1 0.0073
14 124 0.070578 1 0.000795
15 128 0.0679586 1 0.00397
16 140 0.0677785 0.018657 0.00464
17 144 0.0651353 1 0.00768
18 148 0.0623856 1 0.00326
19 160 0.0613706 0.05 0.00881
First-order
Iteration Func-count f(x) Step-size optimality
20 168 0.0604291 0.1 0.0315
21 176 0.0577989 0.5 0.0486
22 184 0.0551139 0.316422 0.0119
23 196 0.0548542 0.0642315 0.012
24 200 0.0546385 1 0.0195
25 204 0.0542103 1 0.0226
26 208 0.0539245 1 0.00859
27 216 0.0537159 0.606052 0.00933
28 220 0.0536754 1 0.0152
29 224 0.0535703 1 0.00595
30 228 0.0535241 1 0.00765
31 232 0.0534898 1 0.000512
32 240 0.0534807 0.637902 0.00356
33 244 0.0534756 1 0.00225
34 248 0.0534715 1 0.000659
35 252 0.0534693 1 0.000843
36 256 0.0534665 1 0.000913
37 260 0.0534629 1 0.000377
38 264 0.0534554 1 0.000711
39 268 0.0534383 1 0.00205
First-order
Iteration Func-count f(x) Step-size optimality
40 272 0.0534173 1 0.000621
41 276 0.0534143 1 4.3e-05
42 284 0.053414 0.462925 0.000647
43 288 0.0534139 1 0.000574
44 292 0.0534137 1 0.000111
45 296 0.0534137 1 2.18e-05
46 300 0.0534137 1 2.18e-05
47 304 0.0534137 1 8.95e-07
Local minimum possible.
fminunc stopped because it cannot decrease the objective function
along the current search direction.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-5.02268 52.5099 -31.6053]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0175974 0.000575
1 20 0.0172335 820 0.000448
2 24 0.0164462 1 8.57e-05
3 28 0.0164101 1 5.82e-05
4 32 0.0163808 1 2.29e-05
5 36 0.0163771 1 1.2e-05
6 40 0.0163766 1 2.29e-06
7 44 0.0163766 1 7.13e-07
8 48 0.0163766 1 8.35e-08
9 52 0.0163766 1 4.92e-09
10 56 0.0163766 1 7.45e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [5.92389 -68.2112 12.6966]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0678991 0.00241
1 24 0.0373439 7381 0.00107
2 28 0.0308493 1 0.000524
3 32 0.0288887 1 0.000104
4 36 0.0288321 1 6.69e-05
5 40 0.0288005 1 4.23e-05
6 60 0.0270363 0.851395 0.00191
7 80 0.0270353 0.000159216 0.00196
8 84 0.0267474 1 0.00163
9 92 0.0265544 0.1 0.00105
10 100 0.0264773 0.430212 0.000502
11 104 0.0264628 1 0.000305
12 108 0.0264579 1 0.000148
13 112 0.0264538 1 3.82e-05
14 116 0.0264491 1 3.72e-05
15 120 0.0264467 1 9.47e-05
16 124 0.0264453 1 5.08e-05
17 128 0.0264435 1 1.71e-05
18 132 0.0264431 1 5.82e-06
19 136 0.026443 1 2.3e-06
First-order
Iteration Func-count f(x) Step-size optimality
20 140 0.026443 1 3.14e-06
21 144 0.026443 1 2.66e-06
22 148 0.026443 1 1.1e-06
23 152 0.026443 1 2e-07
24 156 0.026443 1 1.02e-08
25 160 0.026443 1 2.17e-09
26 164 0.026443 1 6.29e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-56.0343 1.89892 -38.2831]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0383453 0.0984
1 8 0.0342333 1 0.0193
2 12 0.0340572 1 0.0022
3 16 0.0340528 1 0.00216
4 20 0.0339923 1 0.00627
5 32 0.0333662 2.47197 0.0106
6 40 0.0333577 0.15255 0.0145
7 44 0.0332903 1 0.013
8 48 0.0329587 1 0.0165
9 52 0.0326535 1 0.00223
10 60 0.0324975 0.352454 0.0125
11 64 0.0323505 1 0.0116
12 68 0.0320363 1 0.00179
13 76 0.0319071 0.339164 0.0082
14 80 0.0317792 1 0.00757
15 84 0.0315055 1 0.00266
16 92 0.0314097 0.495572 0.00726
17 96 0.0312524 1 0.00596
18 100 0.0310681 1 0.00122
19 108 0.0309783 0.414354 0.00567
First-order
Iteration Func-count f(x) Step-size optimality
20 112 0.0308785 1 0.00479
21 116 0.0307412 1 0.000836
22 124 0.0306819 0.491956 0.00395
23 128 0.0306205 1 0.00295
24 132 0.0305542 1 0.000755
25 140 0.0305303 0.592236 0.00214
26 144 0.0305048 1 0.00106
27 148 0.0304928 1 0.0005
28 152 0.0304888 1 0.000204
29 156 0.0304881 1 0.000193
30 160 0.030488 1 0.000188
31 164 0.0304879 1 0.000197
32 168 0.0304878 1 0.000193
33 172 0.0304868 1 0.000167
34 176 0.0304848 1 0.000168
35 180 0.0304802 1 0.000277
36 184 0.0304735 1 0.00044
37 188 0.0304678 1 0.000406
38 192 0.0304657 1 8.16e-05
39 196 0.0304655 1 3.38e-05
First-order
Iteration Func-count f(x) Step-size optimality
40 200 0.0304655 1 1.76e-06
41 204 0.0304655 1 3.53e-07
42 208 0.0304655 1 2.59e-07
43 212 0.0304655 1 6.94e-09
44 216 0.0304655 1 3.26e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-49.566 -86.2685 -1.39681]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0333736 0.000364
1 24 0.0324429 7381 0.000121
2 28 0.0323603 1 6.67e-06
3 32 0.03236 1 8.09e-07
4 36 0.03236 1 6.23e-09
5 40 0.03236 1 5.12e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [10.0092 -43.6493 43.2024]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.108839 0.00152
1 24 0.0938255 7381 0.000888
2 32 0.0867283 0.680065 0.000224
3 36 0.0862295 1 0.000366
4 48 0.0823211 1.1 0.00313
5 64 0.0495513 0.0817933 0.000854
6 120 0.035256 0.700504 6.51e-05
7 136 0.0352559 0.001 5.59e-05
8 168 0.034298 100.164 0.000395
9 184 0.034298 0.00211688 0.000385
10 200 0.0333551 59.0349 0.000439
11 212 0.0333021 0.0581502 0.000689
12 220 0.0330745 10 0.000901
13 228 0.0327087 0.413887 0.000283
14 232 0.0323748 1 0.000422
15 236 0.0322797 1 0.000401
16 240 0.0320856 1 0.000124
17 244 0.0320746 1 8.6e-05
18 252 0.0320557 10 0.000116
19 256 0.0320406 1 5.1e-05
First-order
Iteration Func-count f(x) Step-size optimality
20 260 0.032038 1 1.35e-05
21 264 0.0320378 1 1.3e-06
22 268 0.0320378 1 5.58e-08
23 272 0.0320378 1 6.21e-09
24 276 0.0320378 1 2.63e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [25.4142 -49.5913 -69.3198]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0901778 0.0017
1 24 0.0763705 7381 0.000575
2 28 0.0746421 1 0.000428
3 32 0.0740793 1 4.56e-05
4 36 0.0740628 1 8.3e-06
5 40 0.0740625 1 4.01e-07
6 44 0.0740625 1 5.31e-08
7 48 0.0740625 1 1.82e-09
8 56 0.0740625 0.456918 8.8e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [27.0966 -70.8436 -14.8188]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.10867 0.00267
1 20 0.103432 820 0.00201
2 28 0.0972792 0.5 0.000667
3 36 0.0957582 0.5 0.000783
4 44 0.0951589 0.1 0.000817
5 52 0.0941367 0.1 0.000741
6 60 0.093522 0.1 0.000636
7 72 0.0933477 0.222976 0.000483
8 76 0.0933103 1 0.00116
9 84 0.0932867 0.236284 0.000197
10 88 0.093274 1 0.000299
11 92 0.0932692 1 8.61e-05
12 96 0.0932691 1 2.38e-05
13 100 0.093269 1 7.06e-07
14 104 0.093269 1 3.14e-07
15 108 0.093269 1 1.48e-07
16 112 0.093269 1 2.85e-08
17 116 0.093269 1 1.71e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-33.2858 -93.6905 13.6487]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0182795 0.000628
1 20 0.0179023 820 0.000493
2 24 0.0172485 1 4.27e-05
3 28 0.0172418 1 1.64e-05
4 32 0.0172404 1 5.73e-06
5 36 0.0172402 1 1.31e-06
6 40 0.0172402 1 9.61e-08
7 44 0.0172402 1 7.06e-09
8 48 0.0172402 1 1.38e-09
9 52 0.0172402 1 1.7e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [23.3647 25.4496 37.0011]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0522176 0.00248
1 20 0.0477406 820 0.00162
2 24 0.0440971 1 0.00208
3 36 0.0386833 0.150835 0.00168
4 48 0.0381368 0.244418 0.00177
5 56 0.0372916 0.244157 0.00123
6 60 0.0371643 1 0.00156
7 64 0.0367725 1 0.000695
8 68 0.0366852 1 0.000998
9 72 0.0366148 1 0.000163
10 76 0.0366016 1 6.76e-05
11 80 0.0365986 1 7.83e-06
12 84 0.0365986 1 4.19e-07
13 88 0.0365986 1 1.35e-08
14 92 0.0365986 1 8.91e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-11.0191 -94.559 30.306]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0473981 0.000565
1 20 0.0470629 820 0.000499
2 24 0.0457017 1 5.34e-05
3 28 0.0456899 1 8.89e-06
4 32 0.0456896 1 1.93e-06
5 36 0.0456896 1 2.43e-07
6 40 0.0456896 1 3.78e-09
7 44 0.0456896 1 9.35e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-12.4495 22.2474 34.2045]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.252666 0.0041
1 24 0.153317 7381 0.0025
2 36 0.148378 0.0715812 0.000438
3 48 0.147235 3.47179 0.000619
4 52 0.146758 1 0.000537
5 60 0.145922 0.413672 0.000405
6 64 0.145407 1 0.000592
7 76 0.144623 0.19 0.000924
8 84 0.144191 0.1 0.00103
9 88 0.143284 1 0.000489
10 92 0.143173 1 0.000237
11 96 0.143086 1 8.89e-05
12 100 0.143077 1 6.99e-05
13 108 0.143024 9.55394 0.000119
14 112 0.142992 1 0.000126
15 120 0.142966 0.304287 9.88e-05
16 124 0.142885 1 0.000129
17 140 0.142839 0.0154322 0.00076
18 152 0.142531 3.63431 0.00416
19 168 0.142497 0.00329606 0.00556
First-order
Iteration Func-count f(x) Step-size optimality
20 184 0.141451 2.17508 0.0141
21 200 0.141389 0.00714644 0.0162
22 220 0.12988 3.439 0.0325
23 232 0.129606 0.01 0.0348
24 236 0.118453 1 0.024
25 240 0.10508 1 0.00872
26 244 0.102087 1 0.00416
27 248 0.100272 1 0.00493
28 252 0.0998848 1 0.00401
29 256 0.0992965 1 0.00283
30 260 0.0985526 1 0.00516
31 264 0.0970957 1 0.00787
32 268 0.0921099 1 0.00418
33 272 0.0912936 1 0.00527
34 276 0.0910867 1 0.00122
35 280 0.0910101 1 0.00147
36 284 0.0908075 1 0.00193
37 288 0.0907305 1 0.000742
38 292 0.0906962 1 0.000726
39 296 0.0906896 1 0.000243
First-order
Iteration Func-count f(x) Step-size optimality
40 300 0.0906879 1 3.6e-05
41 304 0.0906878 1 1.57e-05
42 308 0.0906878 1 2.3e-07
43 312 0.0906878 1 4.4e-08
44 320 0.0906878 0.203412 3.29e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-29.4251 25.6985 -43.707]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0519339 0.000567
1 20 0.0515077 820 0.000497
2 24 0.0501007 1 2.76e-05
3 28 0.050097 1 8.53e-06
4 32 0.0500966 1 4.82e-07
5 36 0.0500966 1 2.88e-08
6 40 0.0500966 1 2.47e-09
7 52 0.0500966 0.070555 9.78e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [14.6958 -0.987663 44.6016]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.103949 0.000279
1 24 0.103558 7381 6.19e-05
2 28 0.103528 1 2.48e-05
3 32 0.103522 1 6.45e-06
4 36 0.103522 1 9.72e-07
5 40 0.103522 1 3.94e-08
6 44 0.103522 1 1.85e-09
7 48 0.103522 1 1.86e-09
8 56 0.103522 0.394032 4.12e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [5.97802 -47.833 13.5648]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0316541 0.000408
1 20 0.0313225 820 0.000368
2 24 0.0304304 1 7.83e-05
3 28 0.0304155 1 2.83e-05
4 32 0.0304139 1 6.75e-06
5 36 0.0304138 1 3.84e-07
6 40 0.0304138 1 2.89e-08
7 44 0.0304138 1 1.82e-09
8 48 0.0304138 1 7.92e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [9.33636 5.65195 66.8186]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.011268 0.000521
1 20 0.0109709 820 0.000451
2 24 0.00972295 1 2.18e-05
3 28 0.00972057 1 5.38e-06
4 32 0.00972042 1 1.27e-07
5 36 0.00972042 1 4.46e-09
6 40 0.00972042 1 3.33e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-25.4933 -36.8466 50.3112]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0248332 0.000397
1 20 0.0246308 820 0.000348
2 24 0.0240304 1 1.95e-05
3 28 0.0240277 1 7.01e-06
4 32 0.0240274 1 1.05e-06
5 36 0.0240274 1 9.53e-08
6 40 0.0240274 1 6.59e-09
7 44 0.0240274 1 1.16e-09
8 56 0.0240274 0.0684778 5.46e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [2.30567 35.8184 29.7051]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0736764 0.000763
1 20 0.0731378 820 0.000541
2 24 0.0725465 1 5.09e-05
3 28 0.0725432 1 7.4e-06
4 32 0.0725431 1 2.4e-07
5 36 0.0725431 1 1.13e-08
6 40 0.0725431 1 4.57e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [49.4014 18.3477 36.4313]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0474348 0.000541
1 20 0.0470527 820 0.000444
2 24 0.0462042 1 4.98e-05
3 28 0.0461926 1 2.75e-05
4 32 0.0461887 1 5.34e-06
5 36 0.0461885 1 1.28e-06
6 40 0.0461885 1 7.18e-08
7 44 0.0461885 1 4.25e-09
8 48 0.0461885 1 2.25e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [45.4151 -20.6746 37.3309]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0494295 0.00137
1 20 0.0470628 820 0.00128
2 24 0.0373869 1 0.000632
3 28 0.0362695 1 0.000527
4 32 0.035371 1 9.05e-05
5 36 0.0353514 1 3.03e-05
6 40 0.0353497 1 2.98e-06
7 44 0.0353497 1 1.21e-07
8 48 0.0353497 1 3.68e-08
9 52 0.0353497 1 4.88e-09
10 56 0.0353497 1 6.98e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-0.166229 55.4867 -18.7225]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.427123 0.000231
1 24 0.426588 7381 0.000135
2 28 0.426354 1 1.02e-05
3 32 0.426353 1 3.28e-06
4 36 0.426353 1 2.71e-07
5 40 0.426353 1 3.36e-08
6 44 0.426353 1 3.73e-09
7 52 0.426353 0.326156 1.49e-09
Local minimum possible.
fminunc stopped because it cannot decrease the objective function
along the current search direction.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [6.25816 7.65795 24.9215]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0514 0.000408
1 20 0.051118 820 0.000347
2 24 0.0504485 1 5.68e-05
3 28 0.0504338 1 3.32e-05
4 32 0.0504258 1 6.44e-06
5 36 0.0504256 1 1.34e-06
6 40 0.0504256 1 1.8e-08
7 44 0.0504256 1 3.77e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-34.6117 16.2878 25.4389]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0246682 0.000276
1 20 0.0245899 820 0.000236
2 24 0.0243768 1 6.2e-06
3 28 0.0243767 1 9.65e-07
4 32 0.0243766 1 4.76e-07
5 36 0.0243766 1 8.91e-08
6 40 0.0243766 1 1.44e-08
7 44 0.0243766 1 8.11e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-34.7937 -54.5162 15.738]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.115313 0.00215
1 24 0.0895636 7381 0.00113
2 28 0.0863827 1 0.00112
3 32 0.078303 1 0.000328
4 36 0.0775808 1 0.000174
5 40 0.0773789 1 5.04e-05
6 44 0.0773658 1 8.84e-06
7 48 0.0773656 1 2.4e-06
8 52 0.0773655 1 2.31e-07
9 56 0.0773655 1 1.71e-08
10 60 0.0773655 1 3.17e-09
11 64 0.0773655 1 4.84e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [30.7616 -76.6934 -28.4696]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0497956 0.000897
1 20 0.0486256 820 0.000601
2 28 0.0480842 0.429545 0.00026
3 36 0.0480679 0.310294 0.000167
4 40 0.0480459 1 0.000128
5 44 0.0480266 1 0.000103
6 48 0.0480236 1 2.55e-05
7 52 0.0480229 1 1.75e-05
8 56 0.0480228 1 3.55e-06
9 60 0.0480228 1 1.42e-07
10 64 0.0480228 1 1.16e-08
11 68 0.0480228 1 1.49e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [64.304 16.7022 8.94435]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.120096 0.00226
1 24 0.0847623 7381 0.0021
2 40 0.0586422 0.14271 0.00337
3 52 0.0577349 0.0621825 0.00371
4 60 0.0552714 0.1 0.00319
5 68 0.050737 0.480364 0.00642
6 80 0.0500154 0.0429748 0.00305
7 88 0.0499696 0.298342 0.000631
8 92 0.0499326 1 0.000656
9 96 0.0498838 1 0.00105
10 100 0.0498693 1 0.000312
11 104 0.0498652 1 2.39e-05
12 108 0.0498651 1 2.16e-05
13 112 0.0498651 1 4.57e-06
14 116 0.0498651 1 1.02e-06
15 120 0.0498651 1 1.9e-07
16 124 0.0498651 1 1.55e-07
17 128 0.0498651 1 2.49e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [64.3942 -30.4266 -28.5422]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0476341 0.000183
1 20 0.0476017 820 0.000152
2 24 0.0475177 1 2.01e-05
3 28 0.0475156 1 1.49e-05
4 32 0.0475138 1 4.42e-06
5 36 0.0475137 1 2.47e-06
6 40 0.0475137 1 2.12e-07
7 44 0.0475137 1 2.54e-08
8 48 0.0475137 1 5.87e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [19.2071 -61.8495 0.397835]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0308479 0.000779
1 20 0.029888 820 0.000627
2 24 0.0276262 1 0.000104
3 28 0.0275858 1 1.44e-05
4 32 0.0275854 1 2.06e-06
5 36 0.0275854 1 3.61e-07
6 40 0.0275854 1 7.58e-09
7 44 0.0275854 1 3.69e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-44.5612 -30.2669 51.1427]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.363298 0.0939
1 8 0.355316 1 0.0127
2 12 0.355152 1 0.00743
3 16 0.354956 1 0.0101
4 20 0.354542 1 0.0147
5 24 0.354066 1 0.0222
6 28 0.353183 1 0.0278
7 32 0.351576 1 0.0195
8 36 0.350435 1 0.00464
9 44 0.350079 0.351587 0.0191
10 48 0.349593 1 0.013
11 52 0.349274 1 0.00232
12 60 0.349154 0.424016 0.00724
13 64 0.349087 1 0.00393
14 68 0.349045 1 0.00136
15 72 0.349035 1 0.00168
16 76 0.349027 1 0.0021
17 80 0.349019 1 0.00311
18 84 0.348979 1 0.00516
19 88 0.348916 1 0.00382
First-order
Iteration Func-count f(x) Step-size optimality
20 92 0.348876 1 0.000465
21 96 0.348865 1 0.0022
22 100 0.348863 1 0.00127
23 104 0.348861 1 1.98e-05
24 108 0.348861 1 1.57e-05
25 112 0.348861 1 4.63e-07
Local minimum possible.
fminunc stopped because it cannot decrease the objective function
along the current search direction.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-47.5661 -85.9279 -0.683024]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.152783 0.00299
1 24 0.146764 455.5 0.00712
2 56 0.127022 0.365704 0.00735
3 76 0.127017 0.00030175 0.00881
4 84 0.124306 2.54286 0.059
5 88 0.122688 1 0.0527
6 92 0.12082 1 0.0442
7 108 0.0985107 2.84741 0.00474
8 112 0.0980138 1 0.0016
9 120 0.0975006 0.478869 0.00602
10 124 0.0972366 1 0.000536
11 128 0.0971753 1 0.000449
12 132 0.097117 1 0.000536
13 136 0.0970949 1 0.000565
14 140 0.0970811 1 0.000918
15 144 0.0970775 1 0.000913
16 148 0.0970744 1 0.00133
17 152 0.0970676 1 0.00145
18 156 0.0970339 1 0.00202
19 160 0.0969727 1 0.00258
First-order
Iteration Func-count f(x) Step-size optimality
20 180 0.0965824 1.855 0.00183
21 192 0.0965613 0.0392331 0.00527
22 196 0.0964822 1 0.00557
23 208 0.0963351 0.353536 0.0115
24 216 0.0961323 0.31401 0.0265
25 220 0.0950285 1 0.0229
26 236 0.0942131 0.271 0.00441
27 248 0.0935861 0.0440803 0.0059
28 256 0.0933479 0.1 0.0172
29 264 0.0931604 0.430049 0.00811
30 272 0.0931516 0.1 0.00524
31 276 0.0931324 1 0.00159
32 280 0.0931242 1 0.00335
33 284 0.0931208 1 0.00361
34 288 0.0931067 1 0.00346
35 292 0.0930865 1 0.00338
36 296 0.0930647 1 0.00527
37 300 0.0930506 1 0.00522
38 304 0.0930359 1 0.00214
39 308 0.0930307 1 0.00265
First-order
Iteration Func-count f(x) Step-size optimality
40 312 0.0930254 1 0.00083
41 316 0.0930241 1 0.00054
42 320 0.0930239 1 0.000177
43 324 0.0930239 1 3.88e-05
44 328 0.0930239 1 1.07e-05
45 332 0.0930239 1 9.43e-07
46 336 0.0930239 1 4.65e-08
47 340 0.0930239 1 8.36e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-61.7592 10.5802 -3.61482]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0257793 0.000381
1 20 0.0256695 820 0.00023
2 24 0.0256004 1 2.28e-05
3 28 0.0255985 1 1.56e-05
4 32 0.0255967 1 6.06e-06
5 36 0.0255966 1 4.64e-06
6 40 0.0255964 1 5.42e-07
7 44 0.0255964 1 5.55e-08
8 48 0.0255964 1 8.05e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [41.3632 -10.7676 54.6387]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.458326 0.0771
1 16 0.399355 5.5 0.0467
2 24 0.396306 0.1 0.0356
3 28 0.391132 1 0.0166
4 32 0.386024 1 0.103
5 36 0.378717 1 0.0556
6 40 0.368706 1 0.0156
7 56 0.324191 1.69659 0.0432
8 76 0.300564 1.02084 0.0182
9 84 0.300541 0.13154 0.0149
10 92 0.298199 0.423065 0.0489
11 96 0.294945 1 0.0205
12 104 0.292059 0.114931 0.0265
13 112 0.288806 0.110393 0.0414
14 120 0.285514 0.152945 0.0596
15 128 0.282001 0.443521 0.111
16 132 0.278994 1 0.0642
17 136 0.275735 1 0.0575
18 140 0.271973 1 0.0614
19 148 0.267857 0.215954 0.0358
First-order
Iteration Func-count f(x) Step-size optimality
20 152 0.267088 1 0.0517
21 156 0.2662 1 0.0425
22 160 0.264944 1 0.022
23 164 0.264072 1 0.0194
24 168 0.26337 1 0.00317
25 172 0.263176 1 0.0109
26 176 0.263002 1 0.00172
27 180 0.262957 1 0.00125
28 184 0.262943 1 0.000954
29 188 0.262936 1 0.000778
30 192 0.262933 1 0.000397
31 196 0.262932 1 9.84e-05
32 200 0.262932 1 9.74e-06
33 204 0.262932 1 3.98e-07
34 208 0.262932 1 6.37e-08
35 220 0.262932 0.0925977 3.95e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [46.9045 60.6006 -5.52853]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0516676 0.000899
1 20 0.0509031 820 0.000678
2 24 0.0498742 1 0.000204
3 28 0.0498099 1 4.69e-05
4 32 0.0498022 1 9.99e-06
5 36 0.0498018 1 6.61e-07
6 40 0.0498018 1 2.68e-08
7 44 0.0498018 1 1.15e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [48.4388 -58.5469 36.5099]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0534839 0.00052
1 20 0.0532465 820 0.000371
2 24 0.0529642 1 3.05e-05
3 28 0.0529605 1 5.49e-06
4 32 0.0529603 1 1.62e-06
5 36 0.0529603 1 4.27e-07
6 40 0.0529603 1 3.59e-08
7 44 0.0529603 1 1.42e-09
8 48 0.0529603 1 4.15e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-53.8345 -40.617 39.6375]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.167046 0.000272
1 20 0.166923 820 0.000234
2 24 0.166534 1 7.72e-06
3 28 0.166533 1 1.93e-06
4 32 0.166533 1 5.04e-07
5 36 0.166533 1 3.47e-07
6 40 0.166533 1 2.02e-08
7 44 0.166533 1 5.01e-09
8 48 0.166533 1 3.24e-09
9 52 0.166533 1 2.45e-09
10 56 0.166533 1 2.21e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-12.9021 -12.6452 37.6532]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.272419 0.000317
1 24 0.271808 7381 8.13e-05
2 28 0.271746 1 1.2e-05
3 32 0.271744 1 4.58e-06
4 36 0.271744 1 4.92e-07
5 40 0.271744 1 1.22e-07
6 44 0.271744 1 2.98e-09
7 48 0.271744 1 2.61e-09
8 60 0.271744 0.0305539 3.35e-09
9 64 0.271744 1 1.86e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-8.02638 -13.3334 31.3034]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.773231 0.00505
1 16 0.770713 91 0.00491
2 20 0.765846 1 0.0244
3 28 0.764731 0.18934 0.0147
4 52 0.711365 1.67567 0.00807
5 56 0.71026 1 0.0195
6 68 0.67868 0.896296 0.0164
7 80 0.675426 0.0197073 0.0135
8 88 0.670214 0.65697 0.00561
9 92 0.66823 1 0.00662
10 100 0.667199 0.1 0.0103
11 104 0.665819 1 0.0118
12 108 0.66285 1 0.00947
13 112 0.661127 1 0.0119
14 124 0.65983 0.0784269 0.00693
15 132 0.659681 0.449471 0.00857
16 136 0.658779 1 0.00717
17 140 0.656587 1 0.027
18 144 0.655164 1 0.0394
19 156 0.653331 0.0748019 0.0031
First-order
Iteration Func-count f(x) Step-size optimality
20 160 0.653253 1 0.00613
21 164 0.652863 1 0.00729
22 168 0.652635 1 0.00404
23 172 0.65253 1 0.00246
24 176 0.652495 1 0.0014
25 180 0.652489 1 0.00105
26 184 0.652488 1 0.000761
27 188 0.652487 1 0.000667
28 192 0.652485 1 0.000351
29 196 0.652481 1 0.000194
30 200 0.652479 1 0.000133
31 204 0.652478 1 5.22e-05
32 208 0.652478 1 1.77e-05
33 212 0.652478 1 1.26e-06
34 216 0.652478 1 4.35e-07
35 220 0.652478 1 1.6e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [69.7745 -56.4083 -18.3684]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.083164 0.000492
1 20 0.0828731 820 0.000395
2 24 0.0818948 1 6.48e-05
3 28 0.0818716 1 2.68e-05
4 32 0.0818676 1 7.69e-07
5 36 0.0818676 1 1.33e-08
6 40 0.0818676 1 2.24e-09
7 48 0.0818676 0.255207 6.11e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-18.3107 1.48886 57.9617]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.879613 0.00423
1 20 0.871802 514.717 0.00763
2 28 0.868116 0.31578 0.00177
3 44 0.852462 2.25294 0.00761
4 48 0.851759 1 0.00798
5 52 0.851376 1 0.00714
6 56 0.848268 1 0.0013
7 60 0.848168 1 0.00157
8 64 0.848098 1 0.00155
9 68 0.8475 1 0.00172
10 92 0.845078 0.40951 0.00437
11 96 0.844675 1 0.00485
12 100 0.840051 1 0.00496
13 116 0.692339 0.452122 0.0221
14 124 0.690483 0.1 0.0223
15 132 0.629591 0.478711 0.0458
16 148 0.614525 0.0178062 0.0639
17 152 0.512243 1 0.0475
18 156 0.509245 1 0.0265
19 172 0.507923 0.00352453 0.00371
First-order
Iteration Func-count f(x) Step-size optimality
20 184 0.50648 2.12819 0.0126
21 188 0.506027 1 0.0143
22 192 0.504184 1 0.0045
23 200 0.503733 0.45746 0.00978
24 204 0.502871 1 0.00967
25 208 0.501796 1 0.00217
26 216 0.501368 0.393778 0.00872
27 220 0.50096 1 0.00794
28 224 0.500427 1 0.00141
29 232 0.500234 0.47183 0.00491
30 236 0.500108 1 0.00375
31 240 0.499981 1 0.000787
32 248 0.499943 0.608175 0.00162
33 252 0.499918 1 0.000644
34 256 0.499898 1 0.000478
35 260 0.499881 1 0.000512
36 264 0.499858 1 0.000823
37 268 0.499837 1 0.000422
38 272 0.499825 1 0.000686
39 276 0.499822 1 0.00046
First-order
Iteration Func-count f(x) Step-size optimality
40 280 0.499819 1 0.000121
41 284 0.499818 1 3.73e-05
42 288 0.499818 1 2e-06
43 292 0.499818 1 7.18e-08
44 296 0.499818 1 1.98e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [79.545 -52.2241 -14.1116]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.62294 0.00484
1 56 0.380016 4590.78 0.00188
2 64 0.378436 0.1 0.00527
3 84 0.377707 0.000372801 0.00345
4 96 0.376922 0.01 0.0108
5 100 0.376067 1 0.0117
6 108 0.375881 0.243547 0.000999
7 112 0.375825 1 0.000316
8 116 0.375778 1 0.00276
9 120 0.375767 1 9.6e-05
10 124 0.375765 1 6.19e-05
11 128 0.375765 1 2.19e-06
12 132 0.375765 1 6e-07
13 140 0.375765 0.305384 4.66e-07
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-68.6393 -40.2533 -20.9633]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.122666 0.000356
1 20 0.122557 820 0.000313
2 24 0.12218 1 7.55e-05
3 28 0.122154 1 5.35e-05
4 32 0.122132 1 5.93e-06
5 36 0.122132 1 1.3e-06
6 40 0.122132 1 1.11e-07
7 44 0.122132 1 8.36e-09
8 48 0.122132 1 1.02e-09
9 52 0.122132 1 4.56e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [40.866 28.8916 24.6329]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.149927 0.00394
1 16 0.148192 91 0.00491
2 24 0.145091 0.1 0.00361
3 32 0.14361 0.385104 0.0165
4 36 0.143307 1 0.00227
5 40 0.143034 1 0.00157
6 48 0.142899 0.64447 0.00301
7 52 0.142848 1 0.00165
8 56 0.142757 1 0.00141
9 60 0.142713 1 0.00196
10 64 0.142615 1 0.00395
11 72 0.142513 0.71699 0.00154
12 76 0.142293 1 0.0039
13 80 0.142205 1 0.023
14 92 0.141823 0.19 0.00904
15 104 0.141795 0.0563905 0.00359
16 108 0.141674 1 0.00203
17 116 0.141555 0.425166 0.0046
18 120 0.141487 1 0.00253
19 124 0.14147 1 0.000827
First-order
Iteration Func-count f(x) Step-size optimality
20 128 0.141468 1 0.00205
21 132 0.141466 1 0.00055
22 140 0.141465 8.46187 0.000899
23 144 0.141464 1 0.000468
24 148 0.141464 1 7.37e-06
25 152 0.141464 1 6.34e-07
26 156 0.141464 1 5.16e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-55.2732 35.7822 23.3205]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.13284 0.056
1 24 0.0754867 14.106 0.0455
2 36 0.0734423 0.0126255 0.0164
3 40 0.0721533 1 0.00984
4 44 0.0704014 1 0.0433
5 48 0.0686124 1 0.0355
6 52 0.0663231 1 0.0043
7 60 0.0650723 0.433636 0.0384
8 64 0.0637381 1 0.0265
9 68 0.0614037 1 0.0558
10 76 0.0568651 0.5 0.013
11 88 0.05634 0.0100083 0.00253
12 96 0.0559791 0.1 0.00897
13 104 0.0557112 0.399411 0.0299
14 112 0.0541998 10 0.0182
15 120 0.0536149 0.613981 0.0435
16 128 0.0526161 0.212839 0.0298
17 132 0.0521593 1 0.0591
18 144 0.0514767 0.05 0.00805
19 156 0.0513102 0.01 0.0394
First-order
Iteration Func-count f(x) Step-size optimality
20 160 0.0512074 1 0.05
21 164 0.0509262 1 0.0914
22 168 0.050726 1 0.0599
23 172 0.0505154 1 0.0424
24 176 0.0501907 1 0.0413
25 180 0.0496973 1 0.0386
26 184 0.0481203 1 0.00197
27 192 0.0476518 0.138987 0.00657
28 200 0.0473785 0.1 0.00295
29 208 0.0472796 0.141858 0.0123
30 212 0.0471509 1 0.0151
31 216 0.0464836 1 0.0298
32 220 0.0453022 1 0.0166
33 228 0.0448701 0.702835 0.0268
34 236 0.0442153 0.644313 0.00934
35 244 0.0441944 0.223095 0.000726
36 248 0.0440992 1 0.00994
37 252 0.0440575 1 0.00527
38 260 0.0440351 0.5 0.00833
39 264 0.0439886 1 0.000995
First-order
Iteration Func-count f(x) Step-size optimality
40 272 0.0439816 0.343171 0.00385
41 276 0.0439758 1 0.00284
42 280 0.043968 1 0.000579
43 288 0.0439665 0.482925 0.0022
44 292 0.0439636 1 0.000517
45 296 0.043963 1 0.000211
46 300 0.0439628 1 0.000261
47 304 0.0439627 1 0.000122
48 308 0.0439627 1 0.000716
49 312 0.0439627 1 0.000463
50 316 0.0439627 1 0.00026
51 320 0.0439625 1 0.000242
52 324 0.0439622 1 0.0008
53 328 0.0439614 1 0.00173
54 332 0.0439601 1 0.00265
55 336 0.0439583 1 0.00291
56 340 0.0439562 1 0.00185
57 344 0.0439551 1 0.000537
58 348 0.043955 1 0.000144
59 352 0.0439549 1 0.00011
First-order
Iteration Func-count f(x) Step-size optimality
60 356 0.0439549 1 6.83e-06
61 360 0.0439549 1 2.32e-06
62 364 0.0439549 1 1.31e-07
63 368 0.0439549 1 6.46e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-70.7765 -12.9222 -10.4781]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.488252 0.00183
1 32 0.482246 1033.24 0.000491
2 40 0.482175 0.425513 0.000757
3 48 0.481865 0.472118 0.000562
4 52 0.48184 1 0.000275
5 56 0.481821 1 0.000286
6 60 0.481802 1 0.0002
7 64 0.481796 1 5.02e-05
8 72 0.481791 0.337413 0.000346
9 76 0.481787 1 1.72e-05
10 80 0.481786 1 4.78e-05
11 84 0.481786 1 4.04e-05
12 88 0.481786 1 7e-06
13 92 0.481786 1 9.26e-07
14 96 0.481786 1 1.07e-08
15 104 0.481786 0.245652 8.5e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [2.08115 -72.2339 63.5561]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.514056 0.0125
1 40 0.316641 914.914 0.0504
2 44 0.285092 1 0.0319
3 60 0.192991 0.271 0.00872
4 76 0.192001 0.00218664 0.0381
5 80 0.188399 1 0.0281
6 88 0.1658 0.367641 0.0102
7 96 0.163508 0.1 0.00725
8 104 0.161822 0.105551 0.0121
9 112 0.157587 0.33347 0.0369
10 120 0.147332 0.637142 0.0588
11 128 0.143982 0.661363 0.0682
12 132 0.140167 1 0.022
13 140 0.138388 0.24486 0.0161
14 144 0.13816 1 0.0333
15 148 0.137559 1 0.0232
16 152 0.136102 1 0.00589
17 164 0.130805 2.01946 0.0086
18 172 0.128774 0.219174 0.0219
19 176 0.123937 1 0.0217
First-order
Iteration Func-count f(x) Step-size optimality
20 180 0.115218 1 0.00267
21 188 0.112112 0.5 0.00656
22 196 0.111925 0.33983 0.0156
23 200 0.111627 1 0.00892
24 204 0.111398 1 0.0084
25 208 0.111292 1 0.00296
26 212 0.111228 1 0.00724
27 216 0.111188 1 0.00127
28 220 0.11115 1 0.00136
29 228 0.111022 0.71017 0.00441
30 232 0.110884 1 0.00205
31 236 0.110643 1 0.00281
32 240 0.110236 1 0.00466
33 244 0.110146 1 0.00421
34 252 0.110101 0.188292 0.00173
35 256 0.110092 1 0.00055
36 260 0.110083 1 0.00102
37 264 0.110078 1 0.00166
38 268 0.110074 1 0.00154
39 272 0.110061 1 0.000709
First-order
Iteration Func-count f(x) Step-size optimality
40 276 0.110059 1 0.000214
41 280 0.110058 1 7.97e-05
42 284 0.110058 1 2.06e-05
43 288 0.110058 1 1.95e-06
44 292 0.110058 1 4.17e-07
45 296 0.110058 1 3.36e-08
46 300 0.110058 1 1.87e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-41.3231 -86.8775 -32.5483]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0535899 0.00077
1 20 0.0531573 820 0.000517
2 24 0.0525394 1 0.000165
3 28 0.0524651 1 7.22e-05
4 32 0.0524591 1 2.74e-05
5 36 0.0524579 1 1.82e-05
6 40 0.0524565 1 1.44e-05
7 44 0.0524563 1 4.56e-06
8 48 0.0524563 1 4.21e-07
9 52 0.0524563 1 2.69e-08
10 56 0.0524563 1 1.8e-09
11 60 0.0524563 1 3.31e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-62.4817 -15.4611 29.899]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.595698 0.039
1 36 0.457823 35.6006 0.00905
2 44 0.456694 0.314677 0.03
3 48 0.433436 1 0.132
4 60 0.39025 0.0181654 0.0179
5 76 0.389316 0.00237221 0.00779
6 80 0.389261 1 0.0085
7 84 0.389037 1 0.0111
8 88 0.389003 1 0.00978
9 92 0.388968 1 0.00817
10 96 0.388934 1 0.00715
11 100 0.388832 1 0.00513
12 104 0.388631 1 0.00292
13 108 0.388256 1 0.0043
14 112 0.387828 1 0.00326
15 116 0.387638 1 0.00137
16 120 0.387616 1 0.0011
17 124 0.387603 1 0.00119
18 128 0.387589 1 0.000921
19 132 0.387584 1 0.000276
First-order
Iteration Func-count f(x) Step-size optimality
20 136 0.387583 1 2.33e-05
21 140 0.387583 1 2.59e-06
22 144 0.387583 1 2.03e-07
Local minimum possible.
fminunc stopped because it cannot decrease the objective function
along the current search direction.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-30.833 65.7422 19.4897]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.357383 0.0292
1 12 0.34887 10 0.0102
2 16 0.347303 1 0.0147
3 20 0.345692 1 0.00771
4 24 0.33708 1 0.0194
5 36 0.329688 0.0835769 0.0364
6 44 0.324173 0.474043 0.041
7 48 0.31214 1 0.0202
8 52 0.30967 1 0.0154
9 60 0.307645 0.241395 0.008
10 68 0.307248 0.315303 0.0129
11 72 0.306641 1 0.011
12 76 0.30447 1 0.00417
13 84 0.304105 0.269054 0.0124
14 88 0.302817 1 0.0156
15 92 0.301961 1 0.0113
16 96 0.30116 1 0.00305
17 100 0.300906 1 0.0104
18 104 0.300758 1 0.00447
19 116 0.300387 2.12258 0.00585
First-order
Iteration Func-count f(x) Step-size optimality
20 124 0.300225 0.262452 0.00844
21 128 0.299824 1 0.00778
22 132 0.299025 1 0.00178
23 140 0.298776 0.130475 0.00378
24 148 0.298573 0.5 0.00564
25 152 0.298355 1 0.00429
26 156 0.298081 1 0.00085
27 164 0.298012 0.5 0.00166
28 168 0.297977 1 0.000926
29 172 0.297966 1 0.000269
30 176 0.297965 1 0.000262
31 180 0.297964 1 0.000255
32 184 0.297963 1 0.000467
33 188 0.297959 1 0.000717
34 192 0.297951 1 0.00088
35 196 0.297945 1 0.000543
36 200 0.297944 1 0.000274
37 204 0.297943 1 2.44e-05
38 208 0.297943 1 9.81e-07
39 212 0.297943 1 9.73e-08
First-order
Iteration Func-count f(x) Step-size optimality
40 216 0.297943 1 6.02e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-45.3491 56.2467 19.171]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.286931 0.544
1 12 0.258841 0.287123 0.11
2 16 0.256652 1 0.0557
3 20 0.255997 1 0.024
4 24 0.255845 1 0.0206
5 28 0.255172 1 0.0272
6 32 0.253476 1 0.0692
7 36 0.250523 1 0.118
8 40 0.247214 1 0.148
9 44 0.241046 1 0.161
10 48 0.224803 1 0.112
11 56 0.21767 0.182328 0.0566
12 72 0.20564 0.990842 0.00462
13 92 0.20563 0.00147251 0.00389
14 112 0.204665 216.89 0.0227
15 116 0.204406 1 0.0357
16 120 0.202976 1 0.0134
17 128 0.202293 0.5 0.0228
18 132 0.201437 1 0.04
19 136 0.199578 1 0.00756
First-order
Iteration Func-count f(x) Step-size optimality
20 144 0.198589 0.495591 0.0301
21 148 0.197856 1 0.041
22 152 0.195948 1 0.00694
23 160 0.19555 0.274477 0.0258
24 164 0.194974 1 0.032
25 168 0.193959 1 0.0141
26 176 0.193566 0.668299 0.0263
27 180 0.192787 1 0.0159
28 184 0.192311 1 0.00975
29 192 0.192128 0.440265 0.0181
30 196 0.19155 1 0.0132
31 204 0.191434 0.517626 0.00374
32 208 0.191264 1 0.00677
33 216 0.191209 0.642337 0.00943
34 220 0.191136 1 0.00608
35 224 0.191081 1 0.00464
36 228 0.191054 1 0.0027
37 236 0.191044 0.5 0.00357
38 240 0.191033 1 0.00263
39 244 0.19102 1 0.00323
First-order
Iteration Func-count f(x) Step-size optimality
40 248 0.191001 1 0.00301
41 252 0.190961 1 0.00556
42 260 0.190939 0.367553 0.00281
43 264 0.190866 1 0.00304
44 268 0.190538 1 0.0036
45 276 0.190091 0.497978 0.0127
46 284 0.189674 0.343942 0.0364
47 288 0.188393 1 0.0134
48 296 0.188177 0.205182 0.0092
49 300 0.188139 1 0.00774
50 304 0.188074 1 0.0043
51 308 0.188063 1 0.00178
52 312 0.18806 1 0.00364
53 316 0.188055 1 0.000768
54 320 0.18805 1 0.000681
55 324 0.188017 1 0.00237
56 328 0.187998 1 0.00363
57 332 0.187994 1 0.000141
58 336 0.187993 1 0.00052
59 340 0.187993 1 0.000578
First-order
Iteration Func-count f(x) Step-size optimality
60 344 0.187992 1 0.000283
61 348 0.187992 1 1.56e-05
62 352 0.187992 1 3.08e-07
63 356 0.187992 1 3.72e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-73.866 -36.8747 -4.84039]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.735488 0.0152
1 44 0.666516 143.056 0.0134
2 60 0.665949 0.005 0.0124
3 84 0.589372 13.0983 0.0373
4 96 0.588854 0.0102331 0.026
5 100 0.585689 1 0.0207
6 120 0.550065 1.2611 0.0264
7 136 0.539769 0.145 0.0268
8 144 0.537336 0.1 0.0396
9 152 0.533219 0.1 0.0309
10 156 0.524575 1 0.0247
11 164 0.522754 0.1 0.045
12 172 0.516918 0.5 0.0779
13 176 0.502907 1 0.0698
14 180 0.496857 1 0.0969
15 196 0.482815 0.158023 0.0281
16 204 0.4825 0.434718 0.0366
17 208 0.480375 1 0.0355
18 212 0.478093 1 0.0271
19 216 0.476965 1 0.0141
First-order
Iteration Func-count f(x) Step-size optimality
20 224 0.476475 0.611013 0.0388
21 228 0.475157 1 0.00698
22 232 0.474823 1 0.0055
23 236 0.474031 1 0.0128
24 244 0.473934 0.309869 0.00993
25 252 0.473849 0.495966 0.00467
26 256 0.473843 1 0.00273
27 260 0.47384 1 0.000442
28 264 0.47384 1 0.000382
29 268 0.47384 1 1.3e-05
30 272 0.47384 1 1.22e-05
31 276 0.47384 1 1.5e-06
32 280 0.47384 1 1.75e-07
33 284 0.47384 1 2.68e-08
34 308 0.47384 1.99819 5.1e-08
Local minimum possible.
fminunc stopped because it cannot decrease the objective function
along the current search direction.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [64.603 36.2347 -2.0246]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.446381 0.0135
1 12 0.443729 10 0.00563
2 16 0.442498 1 0.0105
3 24 0.416167 10 0.0139
4 36 0.413747 0.0103816 0.00777
5 40 0.398209 1 0.0142
6 48 0.398046 0.1 0.00636
7 56 0.396833 10 0.0116
8 60 0.396396 1 0.00661
9 64 0.396359 1 0.00315
10 68 0.396298 1 0.000874
11 72 0.396284 1 0.00117
12 76 0.396278 1 0.000875
13 80 0.396251 1 0.000855
14 84 0.396203 1 0.00202
15 88 0.396156 1 0.00186
16 92 0.396143 1 0.00111
17 96 0.396139 1 0.000455
18 100 0.396138 1 4.37e-05
19 104 0.396138 1 6.6e-06
First-order
Iteration Func-count f(x) Step-size optimality
20 108 0.396138 1 2.62e-06
21 112 0.396138 1 8.01e-07
22 116 0.396138 1 2.79e-08
23 120 0.396138 1 1.47e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [36.2998 22.8647 58.6983]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.387482 0.0437
1 8 0.385163 1 0.0216
2 12 0.383056 1 0.0178
3 20 0.375174 10 0.101
4 24 0.365706 1 0.0737
5 28 0.356361 1 0.00882
6 36 0.353169 0.495362 0.0303
7 40 0.351767 1 0.0186
8 44 0.350886 1 0.00345
9 48 0.350772 1 0.00533
10 52 0.35074 1 0.00135
11 56 0.350737 1 0.000186
12 64 0.350736 10 0.000453
13 68 0.350735 1 0.00058
14 72 0.350733 1 0.000322
15 76 0.350733 1 7.77e-05
16 80 0.350733 1 2.65e-06
17 84 0.350733 1 1.16e-07
18 88 0.350733 1 3.05e-08
19 100 0.350733 0.01 2.24e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-28.2299 69.4621 -1.5417]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.521281 0.0362
1 8 0.518971 1 0.0244
2 12 0.516375 1 0.0364
3 16 0.514878 1 0.0128
4 20 0.512189 1 0.0118
5 24 0.497127 1 0.0178
6 32 0.496442 0.352239 0.0328
7 36 0.495051 1 0.0238
8 40 0.492153 1 0.0164
9 44 0.490402 1 0.00492
10 52 0.48348 0.792912 0.0262
11 64 0.483264 0.01 0.0354
12 68 0.481741 1 0.0348
13 72 0.473019 1 0.0424
14 76 0.464922 1 0.0276
15 84 0.456987 0.436295 0.0129
16 92 0.45651 0.379176 0.0176
17 100 0.442988 9.53866 0.00636
18 108 0.441553 0.207679 0.0126
19 116 0.43895 0.498058 0.0193
First-order
Iteration Func-count f(x) Step-size optimality
20 120 0.434778 1 0.0116
21 124 0.432466 1 0.0285
22 128 0.42597 1 0.0305
23 136 0.423241 0.209546 0.0059
24 140 0.421593 1 0.0125
25 144 0.42098 1 0.0074
26 148 0.420568 1 0.00305
27 152 0.420473 1 0.00125
28 156 0.420458 1 0.000374
29 164 0.420457 0.219804 0.000358
30 168 0.420457 1 0.000306
31 172 0.420456 1 0.000273
32 176 0.420455 1 0.000255
33 180 0.420453 1 0.0003
34 184 0.420452 1 0.000169
35 188 0.420452 1 4.98e-05
36 192 0.420452 1 5.61e-06
37 196 0.420452 1 2.15e-07
38 200 0.420452 1 1.86e-08
39 204 0.420452 1 4.84e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-55.1529 52.9147 -5.23787]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.686049 0.0133
1 28 0.651945 185.861 0.0103
2 48 0.544957 1.30302 0.0252
3 56 0.463107 0.765642 0.0571
4 76 0.450943 0.00239955 0.0712
5 80 0.35908 1 0.104
6 88 0.346313 0.106096 0.137
7 100 0.299271 0.0916682 0.0177
8 104 0.29905 1 0.0132
9 108 0.298725 1 0.00148
10 116 0.298524 10 0.00418
11 128 0.298463 0.047604 0.00788
12 132 0.298372 1 0.00755
13 136 0.297883 1 0.00639
14 140 0.297281 1 0.00353
15 148 0.297137 0.1 0.00808
16 152 0.296739 1 0.0165
17 156 0.296022 1 0.0051
18 164 0.295479 0.718051 0.0127
19 168 0.294909 1 0.0107
First-order
Iteration Func-count f(x) Step-size optimality
20 172 0.294117 1 0.00427
21 180 0.293594 0.21132 0.00571
22 188 0.293445 0.5 0.00794
23 192 0.293157 1 0.00617
24 196 0.292687 1 0.00243
25 204 0.292567 0.462816 0.00534
26 208 0.292361 1 0.00342
27 212 0.292269 1 0.000616
28 220 0.292229 0.450548 0.00194
29 224 0.292212 1 0.00109
30 228 0.292204 1 9.1e-05
31 232 0.292204 1 0.000304
32 236 0.292203 1 9.55e-05
33 240 0.292203 1 6.41e-05
34 244 0.292203 1 4.13e-05
35 248 0.292203 1 1.01e-05
36 252 0.292203 1 1.43e-08
37 256 0.292203 1 1.49e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [81.4216 14.9511 2.64926]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.34729 0.112
1 16 0.278831 2.65712 0.0204
2 24 0.278479 0.29104 0.0153
3 32 0.271722 10 0.0144
4 40 0.264986 0.1 0.00712
5 48 0.261756 0.1 0.0413
6 52 0.260435 1 0.0293
7 56 0.257342 1 0.0932
8 60 0.251839 1 0.0191
9 64 0.247606 1 0.00631
10 72 0.241956 0.460704 0.137
11 88 0.23344 0.0318827 0.00257
12 96 0.23337 0.1 0.00681
13 104 0.233252 10 0.00976
14 112 0.232808 0.40959 0.0246
15 116 0.232341 1 0.0237
16 120 0.230049 1 0.00669
17 128 0.229768 0.103369 0.0154
18 132 0.229349 1 0.0258
19 136 0.22872 1 0.00785
First-order
Iteration Func-count f(x) Step-size optimality
20 140 0.228419 1 0.00866
21 148 0.228253 0.496209 0.0124
22 152 0.228142 1 0.0098
23 156 0.228089 1 0.00974
24 160 0.22801 1 0.00869
25 164 0.227851 1 0.0106
26 168 0.2274 1 0.0101
27 184 0.22251 1.61689 0.0185
28 192 0.222011 0.249122 0.0398
29 200 0.220951 0.152622 0.0402
30 204 0.218831 1 0.0225
31 208 0.217479 1 0.014
32 212 0.216505 1 0.0176
33 216 0.216292 1 0.029
34 224 0.215397 0.255856 0.00146
35 228 0.21535 1 0.0012
36 236 0.215306 0.5 0.00238
37 240 0.215301 1 0.000441
38 244 0.215296 1 0.000137
39 248 0.215296 1 0.000579
First-order
Iteration Func-count f(x) Step-size optimality
40 252 0.215296 1 0.000375
41 256 0.215295 1 4.96e-05
42 260 0.215295 1 2.96e-05
43 264 0.215295 1 7.41e-06
44 268 0.215295 1 9.44e-07
45 272 0.215295 1 2.18e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [58.7271 47.7009 8.0186]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.275904 0.000163
1 24 0.275649 7381 8.65e-05
2 28 0.275561 1 3.94e-05
3 32 0.275545 1 6.46e-06
4 36 0.275544 1 1.5e-06
5 40 0.275544 1 3.91e-07
6 44 0.275544 1 2.18e-07
7 48 0.275544 1 3.65e-08
8 52 0.275544 1 6.33e-09
9 60 0.275544 0.346435 4.47e-09
10 76 0.275544 0.00441857 9.26e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-3.51923 6.73185 40.237]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.743996 0.0112
1 44 0.463246 248.223 0.0111
2 56 0.463123 0.01 0.0285
3 72 0.440708 0.271 0.00969
4 84 0.429251 0.0683034 0.0428
5 92 0.422454 0.168373 0.0136
6 100 0.419441 0.43441 0.1
7 104 0.416219 1 0.226
8 116 0.415179 0.01 0.166
9 128 0.414559 0.0612042 0.0397
10 144 0.411855 28.848 0.0681
11 148 0.41173 1 0.0662
12 152 0.411303 1 0.0164
13 156 0.410981 1 0.021
14 160 0.410553 1 0.0171
15 164 0.410177 1 0.00503
16 168 0.410054 1 0.00698
17 172 0.409967 1 0.0104
18 176 0.409945 1 0.00721
19 180 0.40992 1 0.000712
First-order
Iteration Func-count f(x) Step-size optimality
20 184 0.409917 1 0.000126
21 188 0.409916 1 4.26e-05
22 192 0.409916 1 6.83e-06
23 196 0.409916 1 1.44e-06
24 200 0.409916 1 1.2e-06
25 216 0.409916 0.00461161 1.2e-06
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [0.577278 -71.0883 67.2478]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.483682 0.00404
1 24 0.422952 7381 0.00196
2 36 0.393598 0.881664 0.00212
3 48 0.393507 0.01 0.00403
4 60 0.39312 0.0372248 0.000735
5 68 0.393045 0.1 0.0015
6 72 0.393028 1 0.000347
7 76 0.393024 1 0.000172
8 80 0.393024 1 1.62e-05
9 84 0.393024 1 1.94e-05
10 88 0.393024 1 1.01e-07
11 92 0.393024 1 1.89e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-17.3167 48.5135 -23.6826]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.269174 0.000291
1 20 0.269084 820 0.000261
2 24 0.268742 1 5.32e-05
3 28 0.268713 1 5.65e-05
4 32 0.268671 1 1.53e-05
5 36 0.268669 1 3.21e-06
6 40 0.268669 1 9.76e-08
7 44 0.268669 1 3.75e-09
8 48 0.268669 1 7.5e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-5.97197 -49.644 64.2853]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.305053 0.00155
1 32 0.29941 1034.92 0.00388
2 48 0.299069 0.00528192 0.00389
3 52 0.298514 1 0.00621
4 56 0.297439 1 0.00392
5 64 0.296007 0.5 0.0025
6 68 0.295867 1 0.00114
7 76 0.295726 0.1 0.00166
8 84 0.295651 0.1 0.00191
9 92 0.295597 0.104762 0.00206
10 100 0.295515 0.1 0.00292
11 108 0.295333 0.21818 0.00819
12 112 0.295133 1 0.0183
13 116 0.294699 1 0.00737
14 124 0.294618 0.410808 0.00249
15 128 0.294555 1 0.00249
16 132 0.294475 1 0.00427
17 136 0.294375 1 0.00181
18 140 0.294323 1 0.00132
19 144 0.294244 1 0.00195
First-order
Iteration Func-count f(x) Step-size optimality
20 148 0.294186 1 0.00062
21 152 0.294146 1 0.000469
22 156 0.294127 1 0.00124
23 160 0.294111 1 0.00028
24 164 0.294105 1 0.000232
25 168 0.294101 1 0.000251
26 172 0.294098 1 0.00018
27 176 0.294093 1 0.000362
28 180 0.294086 1 0.000141
29 184 0.294081 1 6.78e-05
30 188 0.294081 1 0.000541
31 192 0.29408 1 0.000201
32 196 0.29408 1 2.21e-05
33 200 0.29408 1 7.57e-06
34 204 0.29408 1 2.4e-06
35 208 0.29408 1 1.45e-07
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-37.2472 -10.8048 71.5842]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.0838166 0.00621
1 16 0.0791915 91 0.00595
2 24 0.0727831 0.144131 0.00556
3 36 0.0691489 0.0861898 0.00281
4 44 0.0689685 0.374473 0.000437
5 48 0.0689471 1 0.000298
6 52 0.068917 1 0.000176
7 56 0.0689116 1 0.000104
8 60 0.0688956 1 0.000372
9 64 0.0688861 1 0.000416
10 68 0.0688787 1 0.000174
11 72 0.0688777 1 2.14e-05
12 76 0.0688776 1 1.74e-06
13 80 0.0688776 1 3.2e-06
14 84 0.0688776 1 1.17e-07
15 88 0.0688776 1 3.07e-09
16 92 0.0688776 1 9.01e-09
Local minimum possible.
fminunc stopped because it cannot decrease the objective function
along the current search direction.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [65.4083 -56.3077 -0.861555]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.418325 0.000115
1 24 0.418141 7381 7.41e-05
2 28 0.418029 1 1.41e-05
3 32 0.418028 1 1.22e-05
4 36 0.418027 1 3.58e-08
5 40 0.418027 1 9.51e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [12.6787 23.5144 21.2144]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.542839 0.0137
1 12 0.541306 8.28092 0.00605
2 20 0.537882 10 0.0191
3 28 0.536645 0.617573 0.032
4 32 0.530957 1 0.0193
5 36 0.522095 1 0.0153
6 44 0.520372 0.1 0.0135
7 52 0.519822 0.1 0.0206
8 56 0.518877 1 0.0357
9 60 0.517817 1 0.00308
10 64 0.517041 1 0.0155
11 72 0.516785 0.599795 0.0252
12 76 0.516067 1 0.00906
13 84 0.515808 0.5 0.0196
14 88 0.515272 1 0.0276
15 96 0.514936 0.441706 0.0112
16 104 0.514603 0.5 0.00911
17 112 0.514541 0.1 0.0374
18 116 0.514385 1 0.024
19 120 0.513626 1 0.106
First-order
Iteration Func-count f(x) Step-size optimality
20 132 0.513319 0.0138761 0.0563
21 136 0.513236 1 0.048
22 140 0.512907 1 0.0151
23 144 0.512655 1 0.0201
24 148 0.512472 1 0.0205
25 152 0.512283 1 0.0136
26 156 0.512138 1 0.00921
27 160 0.512074 1 0.00722
28 164 0.512052 1 0.00522
29 168 0.512046 1 0.0056
30 172 0.512039 1 0.00424
31 176 0.512028 1 0.00344
32 180 0.511974 1 0.00146
33 184 0.511883 1 0.000763
34 188 0.511752 1 0.000977
35 192 0.511671 1 0.00138
36 196 0.511649 1 0.000326
37 200 0.511648 1 0.000722
38 204 0.511647 1 0.00025
39 208 0.511646 1 9.34e-05
First-order
Iteration Func-count f(x) Step-size optimality
40 212 0.511645 1 3.77e-05
41 216 0.511645 1 1.07e-05
42 220 0.511645 1 1.71e-06
43 224 0.511645 1 7.4e-08
44 228 0.511645 1 1.04e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [32.2075 68.7785 2.29738]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.426042 0.11
1 8 0.403662 1 0.102
2 16 0.385986 0.1 0.0707
3 24 0.378447 0.450965 0.00617
4 32 0.373808 10 0.00433
5 40 0.370597 0.391785 0.0271
6 44 0.369266 1 0.0133
7 56 0.364631 1.9 0.0161
8 68 0.364341 0.01 0.0307
9 72 0.363352 1 0.0387
10 76 0.360842 1 0.0992
11 84 0.358775 0.269916 0.0159
12 88 0.357667 1 0.015
13 96 0.35027 0.662273 0.0777
14 100 0.336648 1 0.0559
15 104 0.335755 1 0.0124
16 108 0.334938 1 0.0278
17 112 0.33442 1 0.0312
18 116 0.333891 1 0.0269
19 120 0.332494 1 0.00751
First-order
Iteration Func-count f(x) Step-size optimality
20 124 0.332143 1 0.00788
21 128 0.33203 1 0.00308
22 132 0.332011 1 0.000835
23 136 0.33201 1 5.72e-05
24 140 0.33201 1 6.2e-05
25 144 0.332009 1 1.27e-05
26 148 0.332009 1 1.84e-06
27 152 0.332009 1 2.76e-08
28 156 0.332009 1 1.3e-08
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [23.5107 71.2453 -5.0464]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.686018 0.0135
1 24 0.65614 184.3 0.00744
2 36 0.54159 1.63329 0.0103
3 44 0.415194 0.791196 0.0121
4 56 0.414558 0.0263859 0.00546
5 60 0.41128 1 0.00276
6 68 0.411115 0.5 0.000334
7 72 0.411103 1 0.000156
8 76 0.411099 1 5.33e-06
9 80 0.411099 1 6.72e-06
10 84 0.411099 1 2.11e-06
11 88 0.411099 1 2.1e-07
12 92 0.411099 1 7.99e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-62.0334 54.1125 6.60716]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.173758 0.000265
1 20 0.173662 820 0.000228
2 24 0.173249 1 0.000128
3 28 0.173141 1 0.000111
4 32 0.173012 1 5.95e-05
5 36 0.172987 1 1.18e-05
6 40 0.172985 1 2.17e-06
7 44 0.172985 1 6.58e-07
8 48 0.172985 1 1.16e-07
9 52 0.172985 1 1.51e-08
10 56 0.172985 1 4.19e-09
11 64 0.172985 0.331203 1.94e-09
12 68 0.172985 1 2.24e-10
Local minimum found.
Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-16.6095 -48.0302 58.2143]
the call to "ft_dipolefitting" took 0 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.20146 0.0345
1 28 0.166604 23.3793 0.00855
2 32 0.164515 1 0.0341
3 44 0.151235 0.25 0.0185
4 52 0.150925 0.374269 0.0225
5 56 0.150095 1 0.019
6 60 0.147896 1 0.00647
7 68 0.147308 0.497968 0.00596
8 72 0.14711 1 0.00437
9 76 0.146988 1 0.00165
10 80 0.146963 1 0.00137
11 108 0.145524 104.851 0.00881
12 120 0.14551 0.0273334 0.00416
13 124 0.145355 1 0.00519
14 132 0.145016 0.1 0.0159
15 136 0.144775 1 0.0222
16 140 0.144121 1 0.0126
17 144 0.143542 1 0.00558
18 152 0.143355 0.1 0.00979
19 156 0.143095 1 0.0181
First-order
Iteration Func-count f(x) Step-size optimality
20 160 0.14273 1 0.00842
21 164 0.142394 1 0.0161
22 168 0.141881 1 0.00574
23 176 0.141598 0.487921 0.00355
24 180 0.141171 1 0.00334
25 188 0.141165 0.168368 0.00172
26 192 0.141145 1 0.00263
27 196 0.141131 1 0.00324
28 200 0.141081 1 0.00435
29 204 0.140993 1 0.00436
30 212 0.140908 0.351344 0.00176
31 216 0.140906 1 0.000265
32 220 0.140903 1 0.000401
33 224 0.140899 1 0.000879
34 228 0.140898 1 0.000272
35 232 0.140898 1 2.5e-05
36 236 0.140898 1 5.04e-06
37 240 0.140898 1 1.62e-06
38 244 0.140898 1 4.07e-07
39 252 0.140898 0.245492 3.19e-07
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [71.3855 -37.423 15.7919]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.606634 0.244
1 8 0.576076 1 0.195
2 16 0.571273 0.462546 0.0146
3 28 0.562565 91 0.144
4 32 0.55891 1 0.0968
5 40 0.55619 0.1 0.122
6 48 0.547565 0.664521 0.206
7 52 0.537824 1 0.326
8 64 0.523802 0.19 0.0359
9 72 0.514722 0.1 0.0726
10 80 0.513526 0.221672 0.159
11 84 0.512602 1 0.133
12 88 0.505868 1 0.0752
13 92 0.502107 1 0.0945
14 96 0.495997 1 0.0905
15 104 0.493806 0.1 0.16
16 108 0.492478 1 0.0306
17 112 0.490739 1 0.021
18 116 0.486639 1 0.207
19 120 0.483476 1 0.164
First-order
Iteration Func-count f(x) Step-size optimality
20 124 0.481778 1 0.0332
21 128 0.480334 1 0.0458
22 136 0.48009 0.604834 0.0726
23 140 0.4796 1 0.0497
24 144 0.479045 1 0.0303
25 148 0.478737 1 0.0243
26 152 0.478538 1 0.00573
27 160 0.47844 0.274271 0.0224
28 164 0.478404 1 0.0109
29 168 0.478366 1 0.00631
30 172 0.478355 1 0.00466
31 176 0.478347 1 0.00271
32 180 0.478335 1 0.00261
33 184 0.478309 1 0.00383
34 188 0.478258 1 0.00425
35 192 0.478175 1 0.0213
36 196 0.478079 1 0.0164
37 200 0.477851 1 0.0278
38 204 0.477249 1 0.0882
39 212 0.475286 0.5 0.00607
First-order
Iteration Func-count f(x) Step-size optimality
40 220 0.475123 0.1 0.0352
41 224 0.472936 1 0.0498
42 244 0.451044 1.15874 0.00258
43 260 0.451041 0.00352401 0.00295
44 264 0.450825 1 0.00316
45 272 0.450271 0.469019 0.0111
46 276 0.449917 1 0.00343
47 284 0.449829 0.585711 0.00631
48 288 0.449757 1 0.00304
49 292 0.449752 1 0.00211
50 296 0.44975 1 0.0025
51 300 0.44975 1 0.00256
52 304 0.449746 1 0.00299
53 308 0.44974 1 0.00335
54 312 0.449723 1 0.00395
55 316 0.449685 1 0.00454
56 320 0.449611 1 0.00531
57 324 0.449491 1 0.00684
58 328 0.449293 1 0.0073
59 332 0.449003 1 0.00148
First-order
Iteration Func-count f(x) Step-size optimality
60 340 0.448973 0.5 0.000603
61 344 0.448964 1 0.00027
62 348 0.448964 1 4.41e-05
63 352 0.448964 1 3.3e-05
64 356 0.448964 1 6.84e-06
65 360 0.448964 1 9.66e-08
66 364 0.448964 1 6.77e-08
Local minimum possible.
fminunc stopped because it cannot decrease the objective function
along the current search direction.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [-66.7047 13.5591 20.3175]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.149353 0.0499
1 16 0.136966 5.45525 0.0111
2 20 0.135264 1 0.017
3 24 0.125168 1 0.0471
4 28 0.122249 1 0.0127
5 32 0.120202 1 0.0281
6 36 0.11877 1 0.0145
7 40 0.117968 1 0.0108
8 48 0.117856 0.291479 0.00238
9 52 0.11772 1 0.0063
10 56 0.117696 1 0.000869
11 60 0.117688 1 0.000707
12 64 0.117684 1 0.00083
13 68 0.11768 1 0.000829
14 72 0.11766 1 0.000972
15 76 0.117592 1 0.0025
16 84 0.117476 0.360087 0.00779
17 96 0.11345 0.811715 0.0294
18 112 0.113442 0.00365358 0.0339
19 128 0.095943 18.7311 0.0864
First-order
Iteration Func-count f(x) Step-size optimality
20 148 0.0821381 0.0158832 0.0524
21 156 0.0818751 0.324574 0.0645
22 160 0.079068 1 0.0276
23 164 0.0775078 1 0.0377
24 168 0.0758191 1 0.00507
25 176 0.0753183 0.56499 0.0228
26 180 0.0748409 1 0.0138
27 184 0.0744846 1 0.00285
28 188 0.0743831 1 0.00386
29 192 0.0743578 1 0.00296
30 196 0.0743505 1 0.00277
31 200 0.0743088 1 0.00238
32 204 0.0742274 1 0.0038
33 208 0.074037 1 0.00866
34 212 0.0737622 1 0.0145
35 216 0.0733905 1 0.018
36 220 0.0725552 1 0.0165
37 224 0.0722218 1 0.00891
38 228 0.0721732 1 0.00166
39 232 0.0721589 1 0.00019
First-order
Iteration Func-count f(x) Step-size optimality
40 236 0.0721574 1 0.000506
41 240 0.0721573 1 2.71e-05
42 244 0.0721573 1 2.37e-06
43 248 0.0721573 1 7.28e-09
Local minimum possible.
fminunc stopped because it cannot decrease the objective function
along the current search direction.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [70.8587 -11.3961 22.5665]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
Transforming electrode coordinates to match head model
Recomputing ICA activations
the input is component data with 71 components and 69 original channels
the input is raw data with 71 channels and 5 trials
Warning: use cfg.headmodel instead of cfg.hdmfile
reading 'headmodel' from file 'C:\Users\mathi\OneDrive\Documents\Job\GitHub\MATLAB\Archives\eeglab2021.1\plugins\dipfit\standard_BEM\standard_vol.mat'
using electrodes specified in the data
determining source compartment (3)
projecting electrodes on skin surface
combining electrode transfer and system matrix
selected 69 channels
selected 1 topographies
Warning: the EEG data is not average referenced, correcting this
First-order
Iteration Func-count f(x) Step-size optimality
0 4 0.704933 0.0753
1 8 0.692716 1 0.0663
2 16 0.684154 0.114709 0.043
3 24 0.679351 0.556369 0.0126
4 32 0.664891 10 0.021
5 40 0.662621 0.1 0.0269
6 44 0.654951 1 0.134
7 52 0.651062 0.263336 0.0622
8 56 0.64962 1 0.0317
9 60 0.648945 1 0.0338
10 64 0.648483 1 0.0157
11 68 0.648008 1 0.00558
12 72 0.647681 1 0.0122
13 76 0.647473 1 0.0047
14 80 0.647416 1 0.00109
15 84 0.647415 1 0.000202
16 88 0.647415 1 0.000266
17 92 0.647414 1 0.000262
18 96 0.647413 1 0.000303
19 100 0.64741 1 0.000557
First-order
Iteration Func-count f(x) Step-size optimality
20 104 0.647404 1 0.00224
21 108 0.647391 1 0.00141
22 112 0.647221 1 0.0045
23 120 0.647164 0.24083 0.00779
24 124 0.646961 1 0.00719
25 128 0.646875 1 0.00233
26 136 0.646755 0.601433 0.00725
27 140 0.646587 1 0.00656
28 148 0.646463 0.5 0.000218
29 152 0.646444 1 0.00156
30 156 0.646433 1 0.000762
31 160 0.646431 1 4.91e-05
32 164 0.64643 1 1.16e-05
33 168 0.64643 1 4.74e-06
34 172 0.64643 1 1.07e-07
35 176 0.64643 1 3.13e-09
Local minimum possible.
fminunc stopped because the size of the current step is less than
the value of the step size tolerance.
<stopping criteria details>
found minimum after non-linear optimization for topography 1 on [45.7715 -70.556 49.9519]
the call to "ft_dipolefitting" took 1 seconds
Fail to look up brain areas
rmpath(PathtoEEGLab);
Boundary Element Method (BEM)
clear; clc; close all;
addpath(PathToFieldtrip); ft_defaults;
Here a dataset from Fieldtrip tutorials will be used. More information here: https://www.fieldtriptoolbox.org/tutorial/meg_language/
(Also, see all the datasets from Fieldtrip here: https://www.fieldtriptoolbox.org/faq/datasets/)
mri_data = ft_read_mri('Subject01.mri');
Warning: assuming that the units are "mm"
In ft_estimate_units at line 51
In ft_determine_units at line 113
In ft_read_mri at line 554
% Plot
cfg = []; cfg.method = 'ortho';
ft_sourceplot(cfg, mri_data)
the input is volume data with dimensions [256 256 256]
scaling anatomy to [0 1]
not plotting functional data
not applying a mask on the functional data
not using an atlas
not using a region-of-interest
Warning: no colorbar possible without functional data
In ft_sourceplot>cb_redraw at line 1793
In ft_sourceplot at line 1097
click left mouse button to reposition the cursor
click and hold right mouse button to update the position while moving the mouse
use the arrowkeys to navigate in the current axis
the call to "ft_sourceplot" took 1 seconds
Align the MRI coordinate to the EEG electrodes and source model.
% Check the coordinate system used by the MRI data
ft_determine_coordsys(mri_data)
The positive x-axis is pointing towards anterior
The positive y-axis is pointing towards the left
The positive z-axis is pointing towards superior
The axes are 150 mm long in each direction
The diameter of the sphere at the origin is 10 mm
ans = struct with fields:
dim: [256 256 256]
anatomy: [256×256×256 int16]
hdr: [1×1 struct]
transform: [4×4 double]
unit: 'mm'
coordsys: 'ctf'
% Here, the CTF system is used
% Realign: the positions of the LPA, RPA and nasion must be defined.
cfg = []; cfg.method = 'fiducial';
cfg.fiducial.nas = [87 60 116];
cfg.fiducial.lpa = [29 145 155];
cfg.fiducial.rpa = [144 142 158];
cfg.coordsys = 'ctf'; % the desired coordinate system
mri_data_realigned = ft_volumerealign(cfg, mri_data);
the input is volume data with dimensions [256 256 256]
the call to "ft_volumerealign" took 0 seconds
% Plot
cfg = []; cfg.method = 'ortho'; ft_sourceplot(cfg, mri_data_realigned);
the input is volume data with dimensions [256 256 256]
scaling anatomy to [0 1]
not plotting functional data
not applying a mask on the functional data
not using an atlas
not using a region-of-interest
Warning: no colorbar possible without functional data
In ft_sourceplot>cb_redraw at line 1793
In ft_sourceplot at line 1097
click left mouse button to reposition the cursor
click and hold right mouse button to update the position while moving the mouse
use the arrowkeys to navigate in the current axis
the call to "ft_sourceplot" took 0 seconds
We see that the data is plotted upside down. The MRI image can be resliced:
cfg = []; cfg.method = 'linear';
mri_data_resliced = ft_volumereslice(cfg, mri_data_realigned);
the input is volume data with dimensions [256 256 256]
In getdimord>warning_dimord_could_not_be_determined at line 784
In getdimord at line 734
In ft_volumereslice at line 167
reslicing from [256 256 256] to [256 256 256]
the input is volume data with dimensions [256 256 256]
the input is volume data with dimensions [256 256 256]
selecting subvolume of 90.4%
reslicing and interpolating anatomy
inteinterpolating 100.0
the call to "ft_sourceinterpolate" took 3 seconds
the call to "ft_volumereslice" took 3 seconds
% Plot
cfg = []; cfg.method = 'ortho';
ft_sourceplot(cfg, mri_data_resliced)
the input is volume data with dimensions [256 256 256]
scaling anatomy to [0 1]
not plotting functional data
not applying a mask on the functional data
not using an atlas
not using a region-of-interest
Warning: no colorbar possible without functional data
In ft_sourceplot>cb_redraw at line 1793
In ft_sourceplot at line 1097
click left mouse button to reposition the cursor
click and hold right mouse button to update the position while moving the mouse
use the arrowkeys to navigate in the current axis
the call to "ft_sourceplot" took 0 seconds
For the BEM method, the data must now be segmented to differentiate the scalp, skull, and brain. Each voxel will be attributed to a structure. This step may be long to compute. It is recommended to save the data for future use.
cfg = []; cfg.output = {'brain', 'skull', 'scalp'};
mri_data_segmented_BEM = ft_volumesegment(cfg, mri_data_resliced);
the input is volume data with dimensions [256 256 256]
using 'OldNorm' normalisation
Smoothing by 0 & 8mm..
Coarse Affine Registration..
Fine Affine Registration..
performing the segmentation on the specified volume, using the old-style segmentation
SPM12: spm_preproc (v4916) 12:58:24 - 30/05/2024
========================================================================
Completed : 12:59:56 - 30/05/2024
creating scalpmask ... using the anatomy field for segmentation
smoothing anatomy with a 5-voxel FWHM kernel
thresholding anatomy at a relative threshold of 0.100
creating brainmask ... using the summation of gray, white and csf tpms
smoothing brainmask with a 5-voxel FWHM kernel
thresholding brainmask at a relative threshold of 0.500
creating skullmask ... using the brainmask
the call to "ft_volumesegment" took 127 seconds
% save mri_data_segmented_BEM mri_data_segmented_BEM
ft_checkdata(mri_data_segmented_BEM, 'feedback', 'yes');
the input is segmented volume data with dimensions [256 256 256]
the volume of each of the segmented compartments is
brain : 1573 ml ( 9.37 %)
scalp : 2229 ml ( 13.29 %)
skull : 452 ml ( 2.69 %)
total segmented : 4254 ml ( 25.35 %)
total volume : 16777 ml (100.00 %)
load('mri_data_segmented_BEM.mat')
% Visualization
disp(mri_data_segmented_BEM)
dim: [256 256 256]
transform: [4×4 double]
coordsys: 'ctf'
unit: 'mm'
brain: [256×256×256 logical]
skull: [256×256×256 logical]
scalp: [256×256×256 logical]
cfg: [1×1 struct]
% Plot
% Extract indexes
mri_data_segmented_indexed_BEM = ft_datatype_segmentation(mri_data_segmented_BEM, 'segmentationstyle', 'indexed');
cfg = []; cfg.funparameter = 'tissue';
% cfg.funcolormap = gray(5);
cfg.funcolormap = lines(6);
cfg.location = 'center';
cfg.atlas = mri_data_segmented_indexed_BEM;
ft_sourceplot(cfg, mri_data_segmented_indexed_BEM); %The tissues are numbered according to the order in "tissuelabel"
the input is segmented volume data with dimensions [256 256 256]
the volume of each of the segmented compartments in "tissue" is
scalp : 2229 ml ( 13.29 %)
skull : 452 ml ( 2.69 %)
brain : 1573 ml ( 9.37 %)
total segmented : 4254 ml ( 25.35 %)
total volume : 16777 ml (100.00 %)
not plotting anatomy
not applying a mask on the functional data
not using a region-of-interest
click left mouse button to reposition the cursor
click and hold right mouse button to update the position while moving the mouse
use the arrowkeys to navigate in the current axis
the call to "ft_sourceplot" took 3 seconds
From here, the mesh can be created, which is a triangulated description of the head from vertices connected with each other like triangles. Each triangle will be classified as one of the tissue category.
The "mesh " variable is a structure composed of (1) the position of the vertices (in 3D: samples*x*y*z) and (2) the definition of each corner of a triangle (samples * nb_corners).
cfg = []; cfg.tissue = {'brain', 'skull', 'scalp'};
cfg.numvertices = [3000 2000 1000]; % ratio 3/2/1
mesh_mri_BEM = ft_prepare_mesh(cfg, mri_data_segmented_indexed_BEM);
converting tissue
creating probabilistic representation for scalp
creating probabilistic representation for skull
creating probabilistic representation for brain
triangulating the outer boundary of compartment 1 (brain) with 3000 vertices
triangulating the outer boundary of compartment 2 (skull) with 2000 vertices
triangulating the outer boundary of compartment 3 (scalp) with 1000 vertices
the call to "ft_prepare_mesh" took 4 seconds
% Plot
figure
% Brain
ft_plot_mesh(mesh_mri_BEM(1), 'facecolor','r', 'facealpha', 1.0, 'edgecolor', 'k', 'edgealpha', 1);
hold on
% Skull
ft_plot_mesh(mesh_mri_BEM(2), 'facecolor','g', 'facealpha', 0.4, 'edgecolor', 'k', 'edgealpha', 0.1);
hold on
% Scalp
ft_plot_mesh(mesh_mri_BEM(3), 'facecolor','b', 'facealpha', 0.4, 'edgecolor', 'k', 'edgealpha', 0.1);
view(40,50)
The volume conduction model (head model) can be created.
The head model is a struct array that contains information about (1) positions of the boundaries that is the same as the mesh, (2) the conductivities of the tissues, (3) the potential of every vertice.
cfg = []; cfg.method = 'bemcp'; headmodel_BEM = ft_prepare_headmodel(cfg, mesh_mri_BEM);
In ft_headmodel_bemcp at line 79
In ft_prepare_headmodel at line 310
Nvert = 3000
Ntri = 5996
weight = 0.155225 , defl = 0.000000
Nverta = 2000
Ntrib = 5996
Nverta = 3000
Ntrib = 3996
Nvert = 2000
Ntri = 3996
weight = -0.155225 , defl = 0.000000
Nverta = 1000
Ntrib = 5996
Nverta = 1000
Ntrib = 3996
Nverta = 3000
Ntrib = 1996
Nverta = 2000
Ntrib = 1996
Nvert = 1000
Ntri = 1996
weight = 0.159155 , defl = 0.001000
In ft_prepare_headmodel at line 317
In ft_headmodel_bemcp at line 79
In ft_prepare_headmodel at line 318
Nvert = 3000
Ntri = 5996
weight = 0.155225 , defl = 0.000000
Nverta = 2000
Ntrib = 5996
Nverta = 3000
Ntrib = 3996
Nvert = 2000
Ntri = 3996
weight = -0.155225 , defl = 0.000000
Nverta = 1000
Ntrib = 5996
Nverta = 1000
Ntrib = 3996
Nverta = 3000
Ntrib = 1996
Nverta = 2000
Ntrib = 1996
Nvert = 1000
Ntri = 1996
weight = 0.159155 , defl = 0.001000
the call to "ft_prepare_headmodel" took 34 seconds
disp(headmodel_BEM)
bnd: [1×3 struct]
cond: [0.3300 0.0041 0.3300]
skin_surface: 3
source: 1
mat: [1000×6000 double]
type: 'bemcp'
unit: 'mm'
cfg: [1×1 struct]
% save headmodel_BEM headmodel_BEM
Align with the electrodes
elec_realigned = ft_read_sens('standard_1020.elc');
cfg = []; cfg.tissue = 'scalp';
cfg.numvertices = 10000;
mesh_scalp_BEM = ft_prepare_mesh(cfg, mri_data_segmented_BEM);
triangulating the outer boundary of compartment 1 (scalp) with 10000 vertices
the call to "ft_prepare_mesh" took 2 seconds
% Aligning with the scalp also aligns with lower structures
cfg = []; cfg.method = 'interactive';
cfg.elec = elec_realigned; cfg.headshape = mesh_scalp_BEM;
elec_realigned = ft_electroderealign(cfg);
using electrodes specified in the configuration
Use the mouse to rotate the geometry, and click "redisplay" to update the light.
Close the figure when you are done.
orienting some EEG electrodes along the z-axis
the template coordinate system is "ctf"
the positive X-axis is pointing to anterior
the positive Y-axis is pointing to the left
the positive Z-axis is pointing to superior
the call to "ft_interactiverealign" took 132 seconds
the call to "ft_electroderealign" took 132 seconds
- Rotate -90 around z; apply
- Translate 35 along x and 35 along z; apply
- Scale 0.98 along x, 0.90 along y, and 0.80 along z; apply
- Translate 25 along z; apply
- Rotate 11 degrees around y;
- Translate -5 along x; apply
- Quit
% save elec_realigned_BEM elec_realigned
% Plot
figure
ft_plot_headmodel(headmodel_BEM, 'facecolor', 'skin', 'edgecolor', 'brain','facealpha', 0.7);
this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'
hold on
ft_plot_sens(elec_realigned, 'elecshape', 'sphere', 'label', 'on');
orienting some EEG electrodes along the z-axis
camlight
view(40,50)
Create the source model. Dipoles must be placed in the grey matter.
cfg = []; cfg.resolution = 8; %cfg.threshold = 0.1;
%cfg.smooth = 5;
cfg.headmodel = headmodel_BEM;
cfg.inwardshift = 1; % shifts dipoles away from surfaces
sourcemodel_mri_BEM = ft_prepare_sourcemodel(cfg);
creating sourcemodel based on automatic 3D grid with specified resolution
creating 3D grid with 8 mm resolution
initial 3D grid dimensions are [29 24 32]
2866 dipoles inside, 19406 dipoles outside brain
making tight grid
2866 dipoles inside, 3118 dipoles outside brain
the call to "ft_prepare_sourcemodel" took 1 seconds
% Plot
figure()
ft_plot_mesh(sourcemodel_mri_BEM.pos(sourcemodel_mri_BEM.inside,:))
view(40, 50)
% Plot
figure()
ft_plot_mesh(sourcemodel_mri_BEM.pos(sourcemodel_mri_BEM.inside,:))
view(40,50)
hold on
ft_plot_headmodel(headmodel_BEM, 'facecolor', 'skin', 'edgecolor', 'brain','facealpha', 0);
this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'
view(40, 50)
Select the sources that are in the grey matter
sourcemodel_mri_BEM.pos = sourcemodel_mri_BEM.pos(sourcemodel_mri_BEM.inside,:);
sourcemodel_mri_BEM.inside = sourcemodel_mri_BEM.inside(sourcemodel_mri_BEM.inside,:);
Compute the leadfield matrix. It may be long, save the data for future use.
The leadfield is a structure composed of:
- dim: dimensions of the grid (x*y*z)
- pos: position of the sources (x*y*z)
- unit: unit for position
- inside: 1 the source is inside, 0: the source is outside
- leadfield: each cell defines a source by a matrix of n_channels*x*y*z: potential measured at each electrode in its x*y*z orientation.
- label: electrodes
- leadfielddimord:each cell is a position (x*y*z) defined by a matrix corresponding to channels.
% compute the leadfield
cfg = []; cfg.grid = sourcemodel_mri_BEM;
cfg.headmodel= headmodel_BEM;
cfg.elec = elec_realigned;
cfg.reducerank = 3;
leadfield_mri_bem = ft_prepare_leadfield(cfg);
Warning: use cfg.sourcemodel instead of cfg.grid
In ft_checkconfig at line 130
In ft_prepare_leadfield at line 137
using electrodes specified in the configuration
determining source compartment (1)
projecting electrodes on skin surface
combining electrode transfer and system matrix
creating sourcemodel based on user specified dipole positions
using electrodes specified in the configuration
2866 dipoles inside, 0 dipoles outside brain
the call to "ft_prepare_sourcemodel" took 0 seconds
computing leadfield
computing leadfield 2866/2866
the call to "ft_prepare_leadfield" took 6 seconds
% save leadfield_mri_bem leadfield_mri_bem
disp(leadfield_mri_bem)
dim: [22 16 17]
pos: [2866×3 double]
unit: 'mm'
inside: [2866×1 logical]
cfg: [1×1 struct]
leadfield: {1×2866 cell}
label: {97×1 cell}
leadfielddimord: '{pos}_chan_ori'
Finite Element Method (FEM)
clear; clc; close all;
addpath(PathToFieldtrip); ft_defaults;
Here a dataset from Fieldtrip tutorials will be used. More information here: https://www.fieldtriptoolbox.org/tutorial/meg_language/
(Also, see all the datasets from Fieldtrip here: https://www.fieldtriptoolbox.org/faq/datasets/)
mri_data = ft_read_mri('Subject01.mri');
Warning: assuming that the units are "mm"
In ft_estimate_units at line 51
In ft_determine_units at line 113
In ft_read_mri at line 554
% Plot
cfg = []; cfg.method = 'ortho';
ft_sourceplot(cfg, mri_data)
the input is volume data with dimensions [256 256 256]
scaling anatomy to [0 1]
not plotting functional data
not applying a mask on the functional data
not using an atlas
not using a region-of-interest
Warning: no colorbar possible without functional data
In ft_sourceplot>cb_redraw at line 1793
In ft_sourceplot at line 1097
click left mouse button to reposition the cursor
click and hold right mouse button to update the position while moving the mouse
use the arrowkeys to navigate in the current axis
the call to "ft_sourceplot" took 0 seconds
Align the MRI coordinate to the EEG electrodes and source model.
% Check the coordinate system used by the MRI data
ft_determine_coordsys(mri_data)
The positive x-axis is pointing towards anterior
The positive y-axis is pointing towards the left
The positive z-axis is pointing towards superior
The axes are 150 mm long in each direction
The diameter of the sphere at the origin is 10 mm
ans = struct with fields:
dim: [256 256 256]
anatomy: [256×256×256 int16]
hdr: [1×1 struct]
transform: [4×4 double]
unit: 'mm'
coordsys: 'ctf'
% Here, the CTF system is used
% Realign: the positions of the LPA, RPA and nasion must be defined.
cfg = []; cfg.method = 'fiducial';
cfg.fiducial.nas = [87 60 116];
cfg.fiducial.lpa = [29 145 155];
cfg.fiducial.rpa = [144 142 158];
cfg.coordsys = 'ctf'; % the desired coordinate system
mri_data_realigned = ft_volumerealign(cfg, mri_data);
the input is volume data with dimensions [256 256 256]
the call to "ft_volumerealign" took 0 seconds
% Plot
cfg = []; cfg.method = 'ortho'; ft_sourceplot(cfg, mri_data_realigned);
the input is volume data with dimensions [256 256 256]
scaling anatomy to [0 1]
not plotting functional data
not applying a mask on the functional data
not using an atlas
not using a region-of-interest
Warning: no colorbar possible without functional data
In ft_sourceplot>cb_redraw at line 1793
In ft_sourceplot at line 1097
click left mouse button to reposition the cursor
click and hold right mouse button to update the position while moving the mouse
use the arrowkeys to navigate in the current axis
the call to "ft_sourceplot" took 0 seconds
We see that the data is plotted upside down. The MRI image can be resliced:
cfg = []; cfg.method = 'flip'; %linear
mri_data_resliced = ft_volumereslice(cfg, mri_data_realigned);
the input is volume data with dimensions [256 256 256]
In getdimord>warning_dimord_could_not_be_determined at line 784
In getdimord at line 734
In ft_volumereslice at line 167
the call to "ft_volumereslice" took 0 seconds
% Plot
cfg = []; cfg.method = 'ortho'; ft_sourceplot(cfg, mri_data_resliced)
the input is volume data with dimensions [256 256 256]
scaling anatomy to [0 1]
not plotting functional data
not applying a mask on the functional data
not using an atlas
not using a region-of-interest
Warning: no colorbar possible without functional data
In ft_sourceplot>cb_redraw at line 1793
In ft_sourceplot at line 1097
click left mouse button to reposition the cursor
click and hold right mouse button to update the position while moving the mouse
use the arrowkeys to navigate in the current axis
the call to "ft_sourceplot" took 0 seconds
For the FEM method, the data must now be segmented to differentiate the scalp, skull, cerebro-spinal fluid, gray, and white matters. Each voxel will be attributed to a structure. This step may be long to compute. It is recommended to save the data for future use.
cfg = []; cfg.output = {'gray', 'white', 'csf', 'skull', 'scalp'};
% cfg.brainsmooth = 1; cfg.scalpthreshold = 0.11; cfg.skullthreshold = 0.15;
% cfg.brainthreshold = 0.15;
mri_data_segmented = ft_volumesegment(cfg, mri_data_resliced);
the input is volume data with dimensions [256 256 256]
using 'OldNorm' normalisation
Smoothing by 0 & 8mm..
Coarse Affine Registration..
Fine Affine Registration..
performing the segmentation on the specified volume, using the old-style segmentation
SPM12: spm_preproc (v4916) 13:25:35 - 30/05/2024
========================================================================
Completed : 13:30:32 - 30/05/2024
creating scalpmask ... using the anatomy field for segmentation
smoothing anatomy with a 5-voxel FWHM kernel
thresholding anatomy at a relative threshold of 0.100
creating brainmask ... using the summation of gray, white and csf tpms
smoothing brainmask with a 5-voxel FWHM kernel
thresholding brainmask at a relative threshold of 0.500
creating skullmask ... using the brainmask
the call to "ft_volumesegment" took 331 seconds
% save mri_data_segmented_FEM mri_data_segmented
load('mri_data_segmented_FEM.mat')
% Visualization
disp(mri_data_segmented)
dim: [256 256 256]
transform: [4×4 double]
coordsys: 'ctf'
unit: 'mm'
gray: [256×256×256 logical]
white: [256×256×256 logical]
csf: [256×256×256 logical]
skull: [256×256×256 logical]
scalp: [256×256×256 logical]
cfg: [1×1 struct]
% Plot
% Extract indexes
mri_data_segmented_indexed = ft_datatype_segmentation(mri_data_segmented, 'segmentationstyle', 'indexed');
Warning: more than 4 tissue types, this may take a while
In fixsegmentation at line 68
In ft_datatype_segmentation at line 170
cfg = []; cfg.funparameter = 'tissue';
% cfg.funcolormap = gray(5);
cfg.funcolormap = lines(6);
cfg.location = 'center';
cfg.atlas = mri_data_segmented_indexed;
ft_sourceplot(cfg, mri_data_segmented_indexed); %The tissues are numbered according to the order in "tissuelabel"
the input is segmented volume data with dimensions [256 256 256]
the volume of each of the segmented compartments in "tissue" is
csf : 274 ml ( 0.84 %)
gray : 777 ml ( 2.37 %)
scalp : 2395 ml ( 7.31 %)
skull : 565 ml ( 1.73 %)
white : 485 ml ( 1.48 %)
total segmented : 4496 ml ( 13.72 %)
total volume : 32768 ml (100.00 %)
not plotting anatomy
not applying a mask on the functional data
not using a region-of-interest
click left mouse button to reposition the cursor
click and hold right mouse button to update the position while moving the mouse
use the arrowkeys to navigate in the current axis
the call to "ft_sourceplot" took 3 seconds
From here, the mesh can be created, which is a hexahedral description of the head from 8 vertices connected with each other like cubles. Each hexahedron will be classified as one of the tissue category.
The "mesh " variable is a structure composed of (1) the position of the vertices (in 3D: samples*x*y*z), (2) the definition of each corner of a hexahedron (samples * nb_corners), (3) the tissue assigned for each hexahedron, (4) labels of the tissues defined during segmentation, (5) the unit to express the vertices, (6) the coordinates system.
cfg = []; cfg.shift = 0.3;
cfg.method = 'hexahedral';
% cfg.resolution = 1; % mm
mesh_mri = ft_prepare_mesh(cfg, mri_data_segmented);
Warning: more than 4 tissue types, this may take a while
In fixsegmentation at line 68
In ft_datatype_segmentation at line 170
In ft_checkdata at line 285
In prepare_mesh_hexahedral at line 18
In ft_prepare_mesh at line 170
using the field "tissue" with the segmented tissue types csf, gray, scalp, skull and white
Created elements...
Created nodes...
Applying shift 0.300000
the call to "ft_prepare_mesh" took 25 seconds
disp(mesh_mri)
hex: [2301983×8 double]
pos: [2372249×3 double]
tissue: [2301983×1 double]
tissuelabel: {'csf' 'gray' 'scalp' 'skull' 'white'}
unit: 'mm'
coordsys: 'ctf'
cfg: [1×1 struct]
% save mesh_mri mesh_mri
load('mesh_mri.mat')
The volume conduction model (head model) can be created.
The conductivities of each tissue must be defined following the order of labels of "mesh_mri.tissuelabel" (here, 'csf', 'gray', 'scalp', 'skull', 'white').
The head model is a struct array that contains information about (1) pos, (2) hex, (3) tissue, (4) tissulabel, and (5) the unit also contained in the mesh, and (6) cond: the conductivity of every tissue, (7) stiff: sparse matrix of head model, and (8) type: the method used.
cfg = []; cfg.method = 'simbio';
cfg.conductivity = [1.79 0.33 0.43 0.01 0.14];
headmodel_mri = ft_prepare_headmodel(cfg, mesh_mri);
the call to "ft_prepare_headmodel" took 34 seconds
disp(headmodel_mri)
pos: [2372249×3 double]
hex: [2301983×8 double]
tissue: [2301983×1 double]
cond: [1.7900 0.3300 0.4300 0.0100 0.1400]
tissuelabel: {'csf' 'gray' 'scalp' 'skull' 'white'}
stiff: [2372249×2372249 double]
type: 'simbio'
unit: 'mm'
cfg: [1×1 struct]
% save headmodel_mri headmodel_mri
load('headmodel_mri')
% Plot the mesh
figure()
ft_plot_mesh(mesh_mri, 'edgecolor','none', 'facecolor', 'skin', 'facealpha', 0.7);
Warning: only visualizing the outer surface of the hexahedral mesh, see the "surfaceonly" option
In ft_plot_mesh at line 132
this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'
ft_plot_axes(mesh_mri)
The axes are 150 mm long in each direction
The diameter of the sphere at the origin is 10 mm
alpha 1
material default
camlight
view(40,50)
% Plot the mesh according to tissue
% Option 1
figure()
% make a copy for each tissue type
% after splitting we don't need the tissue and tissuelabel any more
mesh_mri_csf = rmfield(mesh_mri, {'tissue', 'tissuelabel'});
mesh_mri_gray = rmfield(mesh_mri, {'tissue', 'tissuelabel'});
mesh_mri_scalp = rmfield(mesh_mri, {'tissue', 'tissuelabel'});
mesh_mri_skull = rmfield(mesh_mri, {'tissue', 'tissuelabel'});
mesh_mri_white = rmfield(mesh_mri, {'tissue', 'tissuelabel'});
% only keep the hexahedra for the corresponding tissue type
mesh_mri_csf.hex = mesh_mri.hex(mesh_mri.tissue==1,:);
mesh_mri_gray.hex = mesh_mri.hex(mesh_mri.tissue==2,:);
mesh_mri_scalp.hex = mesh_mri.hex(mesh_mri.tissue==3,:);
mesh_mri_skull.hex = mesh_mri.hex(mesh_mri.tissue==4,:);
mesh_mri_white.hex = mesh_mri.hex(mesh_mri.tissue==5,:);
% Plot gray
ft_plot_mesh(mesh_mri_gray, 'edgecolor','none', 'facecolor', 'skin', 'facealpha', 0.7);
Warning: only visualizing the outer surface of the hexahedral mesh, see the "surfaceonly" option
In ft_plot_mesh at line 132
ft_plot_axes(mesh_mri_gray)
The axes are 150 mm long in each direction
The diameter of the sphere at the origin is 10 mm
alpha 1
material default
camlight
view(40,50)
% Option 2 : you can see that the head model is not algined with the
% electrodes
figure()
elec_realigned = ft_read_sens('standard_1020.elc');
tissue_plot = 3; %scalp
mesh2 =[]; mesh2.hex = headmodel_mri.hex(headmodel_mri.tissue==tissue_plot,:);
mesh2.pos = headmodel_mri.pos;
mesh2.tissue = headmodel_mri.tissue(headmodel_mri.tissue==tissue_plot,:);
mesh_ed = mesh2edge(mesh2);
patch('Faces',mesh_ed.poly,...
'Vertices',mesh_ed.pos,...
'FaceAlpha',.5,...
'LineStyle', 'none',...
'FaceColor',[1 1 1],...
'FaceLighting', 'gouraud');
xlabel('coronal');ylabel('sagital');zlabel('axial');camlight;
axis on; ft_plot_sens(elec_realigned, 'style', '*g');
orienting some EEG electrodes along the z-axis
view(40,50)
Align with the electrodes
elec_realigned = ft_read_sens('standard_1020.elc');
cfg = [];
cfg.method = 'interactive';
cfg.headshape = mesh_mri; % we can specify the hexahedral FEM mesh as the headshape
elec_realigned = ft_electroderealign(cfg, elec_realigned)
extracting surface from hexahedral mesh
Use the mouse to rotate the geometry, and click "redisplay" to update the light.
Close the figure when you are done.
orienting some EEG electrodes along the z-axis
Warning: only visualizing the outer surface of the hexahedral mesh, see the "surfaceonly" option
In ft_plot_mesh at line 132
In ft_plot_headshape at line 102
In ft_interactiverealign>cb_redraw at line 403
In ft_interactiverealign at line 208
In ft_electroderealign at line 574
the template coordinate system is "ctf"
the positive X-axis is pointing to anterior
the positive Y-axis is pointing to the left
the positive Z-axis is pointing to superior
the call to "ft_interactiverealign" took 102 seconds
the call to "ft_electroderealign" took 105 seconds
elec_realigned = struct with fields:
chanpos: [97×3 double]
chantype: {97×1 cell}
chanunit: {97×1 cell}
elecpos: [97×3 double]
homogeneous: [4×4 double]
label: {97×1 cell}
tra: [97×97 double]
type: 'eeg1010'
unit: 'mm'
cfg: [1×1 struct]
- Rotate -90 around z; apply
- Translate 35 along x and 35 along z; apply
- Scale 0.98 along x, 0.90 along y, and 0.80 along z; apply
- Translate 25 along z; apply
- Rotate 11 degrees around y;
- Translate -5 along x; apply
- Quit
% Option 1
figure()
ft_plot_headmodel(headmodel_mri, 'facecolor', 'skin', 'edgecolor', 'brain','facealpha', 0.7);
this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'
hold on
ft_plot_sens(elec_realigned, 'elecshape', 'sphere', 'label', 'on');
orienting some EEG electrodes along the z-axis
camlight
view(40,50)
% Option 2
figure()
tissue_plot = 3; %scalp
mesh2 =[]; mesh2.hex = headmodel_mri.hex(headmodel_mri.tissue==tissue_plot,:);
mesh2.pos = headmodel_mri.pos;
mesh2.tissue = headmodel_mri.tissue(headmodel_mri.tissue==tissue_plot,:);
mesh_ed = mesh2edge(mesh2);
patch('Faces',mesh_ed.poly,...
'Vertices',mesh_ed.pos,...
'FaceAlpha',.5,...
'LineStyle', 'none',...
'FaceColor',[1 1 1],...
'FaceLighting', 'gouraud');
xlabel('coronal'); ylabel('sagital'); zlabel('axial')
camlight; axis on;ft_plot_sens(elec_realigned, 'style', '*g');
orienting some EEG electrodes along the z-axis
view(40,50)
Create the source model. Dipoles must be placed in the grey matter.
% cfg = []; cfg.method = 'basedonresolution';
% cfg.resolution = 8;
% cfg.unit = 'mm'; cfg.headmodel = mesh_mri;
% cfg.headmodel.type = 'simbio'; cfg.movetocentroids = 'yes';
% % Dipoles are moved to the centroids of the volume elements
% sourcemodel_mri = ft_prepare_sourcemodel(cfg)
% Specify the initial 3D grid for less computation time.
% Approximate bounderies of the brain must be known.
% cfg = []; cfg.method = 'basedongrid';
% cfg.xgrid = -80:8:120; % multiples of 8
% cfg.ygrid = -80:8:80; cfg.zgrid = -24:8:120;
% cfg.unit = 'mm'; cfg.headmodel = mesh_mri;
% cfg.headmodel.type = 'simbio'; cfg.movetocentroids = 'yes';
% sourcemodel_mri = ft_prepare_sourcemodel(cfg)
cfg = []; cfg.resolution = 8; %cfg.threshold = 0.1;
%cfg.smooth = 5;
cfg.headmodel = headmodel_mri;
cfg.inwardshift = 1; % shifts dipoles away from surfaces
sourcemodel_mri = ft_prepare_sourcemodel(cfg);
creating sourcemodel based on automatic 3D grid with specified resolution
creating 3D grid with 8 mm resolution
initial 3D grid dimensions are [31 24 37]
selecting dipole positions inside the 'gray' tissue
pruning headmodel volume elements from 2301983 to 397830 (17%)
pruning headmodel vertices from 2372249 to 583565 (25%)
pruning dipole positions from 27528 to 5984 (22%)
1554 dipoles inside, 25974 dipoles outside brain
making tight grid
1554 dipoles inside, 4430 dipoles outside brain
the call to "ft_prepare_sourcemodel" took 2 seconds
% Plot the sources: only grey matter
figure()
ft_plot_mesh(sourcemodel_mri.pos(sourcemodel_mri.inside,:))
hold on,
ft_plot_mesh(mesh_mri, 'surfaceonly', 'yes', 'vertexcolor', 'none', 'facecolor',...
'skin', 'facealpha',0.5, 'edgealpha',0.1)
this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'
view(40,50)
% Plot the sources: Whole source model. There is a lot of dipoles outside
% the grey matter
figure()
ft_plot_mesh(sourcemodel_mri); ft_plot_axes(sourcemodel_mri)
The axes are 150 mm long in each direction
The diameter of the sphere at the origin is 10 mm
hold on,
ft_plot_mesh(mesh_mri, 'surfaceonly', 'yes', 'vertexcolor', 'none', 'facecolor',...
'skin', 'facealpha',0.5, 'edgealpha',0.1)
view(40,50)
Select the sources that are in the grey matter
sourcemodel_mri.pos = sourcemodel_mri.pos(sourcemodel_mri.inside,:);
sourcemodel_mri.inside = sourcemodel_mri.inside(sourcemodel_mri.inside,:);
Compute the leadfield matrix. It may be long, save the data for future use.
The leadfield is a structure composed of:
- dim: dimensions of the grid (x*y*z)
- pos: position of the sources (x*y*z)
- unit: unit for position
- inside: 1 the source is inside, 0: the source is outside
- leadfield: each cell defines a source by a matrix of n_channels*x*y*z: potential measured at each electrode in its x*y*z orientation.
- label: electrodes
- leadfielddimord:each cell is a position (x*y*z) defined by a matrix corresponding to channels.
% transfer matrix
[headmodel_mri, elec_realigned] = ft_prepare_vol_sens(headmodel_mri, elec_realigned);
Find electrode positions...
Calculate transfer matrix...
Electrode 2 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.5008e-09
Electrode 3 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7363e-09
Electrode 4 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6526e-09
Electrode 5 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.0181e-09
Electrode 6 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2729e-09
Electrode 7 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 8.9899e-09
Electrode 8 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6933e-09
Electrode 9 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9075e-09
Electrode 10 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4141e-09
Electrode 11 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.0756e-09
Electrode 12 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2423e-09
Electrode 13 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.5747e-09
Electrode 14 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4460e-09
Electrode 15 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9580e-09
Electrode 16 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6332e-09
Electrode 17 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.8360e-09
Electrode 18 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.5470e-09
Electrode 19 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9959e-09
Electrode 20 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.1986e-09
Electrode 21 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2483e-09
Electrode 22 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4427e-09
Electrode 23 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2825e-09
Electrode 24 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.5995e-09
Electrode 25 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.8788e-09
Electrode 26 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.0498e-09
Electrode 27 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.1680e-09
Electrode 28 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.8647e-09
Electrode 29 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7906e-09
Electrode 30 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.8573e-09
Electrode 31 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 8.9174e-09
Electrode 32 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9187e-09
Electrode 33 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2750e-09
Electrode 34 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.8967e-09
Electrode 35 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9410e-09
Electrode 36 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6301e-09
Electrode 37 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.0660e-09
Electrode 38 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7893e-09
Electrode 39 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9287e-09
Electrode 40 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.5346e-09
Electrode 41 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.1313e-09
Electrode 42 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6882e-09
Electrode 43 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7847e-09
Electrode 44 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6496e-09
Electrode 45 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7844e-09
Electrode 46 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7584e-09
Electrode 47 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.3385e-09
Electrode 48 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 8.8302e-09
Electrode 49 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.0776e-09
Electrode 50 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.5446e-09
Electrode 51 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9385e-09
Electrode 52 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2488e-09
Electrode 53 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6002e-09
Electrode 54 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.0632e-09
Electrode 55 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.8204e-09
Electrode 56 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4906e-09
Electrode 57 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4504e-09
Electrode 58 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6076e-09
Electrode 59 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.1741e-09
Electrode 60 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7333e-09
Electrode 61 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.3756e-09
Electrode 62 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.5660e-09
Electrode 63 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.1005e-09
Electrode 64 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6502e-09
Electrode 65 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4995e-09
Electrode 66 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7286e-09
Electrode 67 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6012e-09
Electrode 68 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4959e-09
Electrode 69 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2120e-09
Electrode 70 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.3961e-09
Electrode 71 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.3528e-09
Electrode 72 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9246e-09
Electrode 73 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.8051e-09
Electrode 74 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.3297e-09
Electrode 75 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 8.9870e-09
Electrode 76 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7050e-09
Electrode 77 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.8693e-09
Electrode 78 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6433e-09
Electrode 79 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4049e-09
Electrode 80 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9395e-09
Electrode 81 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.5635e-09
Electrode 82 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.1408e-09
Electrode 83 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7920e-09
Electrode 84 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2635e-09
Electrode 85 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.7693e-09
Electrode 86 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.6765e-09
Electrode 87 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.3224e-09
Electrode 88 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 8.6645e-09
Electrode 89 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.2071e-09
Electrode 90 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.1313e-09
Electrode 91 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.1005e-09
Electrode 92 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.0776e-09
Electrode 93 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.3528e-09
Electrode 94 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4584e-09
Electrode 95 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.9626e-09
Electrode 96 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 8.8447e-09
Electrode 97 of 97
Scaling stiffnes matrix...
Preconditioning...
Finding startvector...
Solving equation system...
rr = 9.4512e-09
% compute the leadfield
cfg = []; cfg.grid = sourcemodel_mri;
cfg.headmodel= headmodel_mri;
cfg.elec = elec_realigned;
cfg.reducerank = 3;
leadfield_mri_fem = ft_prepare_leadfield(cfg);
Warning: use cfg.sourcemodel instead of cfg.grid
In ft_checkconfig at line 130
In ft_prepare_leadfield at line 137
using electrodes specified in the configuration
creating sourcemodel based on user specified dipole positions
using electrodes specified in the configuration
1554 dipoles inside, 0 dipoles outside brain
the call to "ft_prepare_sourcemodel" took 1 seconds
computing leadfield
computing leadfield 1554/1554
the call to "ft_prepare_leadfield" took 269 seconds
% save leadfield_mri_fem leadfield_mri_fem
disp(leadfield_mri_fem)
dim: [22 16 17]
pos: [1554×3 double]
unit: 'mm'
inside: [1554×1 logical]
cfg: [1×1 struct]
leadfield: {1×1554 cell}
label: {97×1 cell}
leadfielddimord: '{pos}_chan_ori'
Source reconstruction of event-related potentials
Minimum-norm estimation
Generally applied to wide-spread cortical activity and assumes a large number of dipoles.
Goal
A representation of the neuronal activity underlying event-related components that resulted in the topography of the activity observed at the sensors is calculated.
Here, the activity of the source of the ERP computed in the "EventRelatedPotentials.mlx" document will be extracted.
load('EEG_epoched_filt.mat')
% Cond1 - participant 1
EEG_cond1 = struct(); EEG_cond1.label = {EEG_epoched_filt{1,1}.chanlocs.labels}';
data = EEG_epoched_filt{1,1}.data;
for i = 1:1:size(data,3)
EEG_cond1_trial{1,i} = data(:,:,i);
EEG_cond1_time{1,i} = EEG_epoched_filt{1,1}.times;
end
EEG_cond1.trial = EEG_cond1_trial; EEG_cond1.time = EEG_cond1_time;
EEG_cond1.fsample = EEG_epoched_filt{1,1}.srate;
% Cond2 - participant 1
EEG_cond2 = struct(); EEG_cond2.label = {EEG_epoched_filt{2,1}.chanlocs.labels}';
data = EEG_epoched_filt{2,1}.data;
for i = 1:1:size(data,3)
EEG_cond1_trial{1,i} = data(:,:,i);
EEG_cond1_time{1,i} = EEG_epoched_filt{2,1}.times;
end
EEG_cond2.trial = EEG_cond1_trial; EEG_cond2.time = EEG_cond1_time;
EEG_cond2.fsample = EEG_epoched_filt{2,1}.srate;
% Average over trials
cfg = []; cfg.covariance = 'yes'; cfg.covariancewindow = [-inf 0];
avgCond1 = ft_timelockanalysis(cfg, EEG_cond1);
the input is raw data with 24 channels and 240 trials
In fixsampleinfo at line 92
In ft_datatype_raw at line 148
In ft_checkdata at line 277
In ft_timelockanalysis at line 98
In fixsampleinfo at line 105
In ft_datatype_raw at line 148
In ft_checkdata at line 277
In ft_timelockanalysis at line 98
the call to "ft_selectdata" took 0 seconds
the call to "ft_selectdata" took 0 seconds
the call to "ft_timelockanalysis" took 1 seconds
avgCond2 = ft_timelockanalysis(cfg, EEG_cond2);
the input is raw data with 24 channels and 240 trials
In fixsampleinfo at line 92
In ft_datatype_raw at line 148
In ft_checkdata at line 277
In ft_timelockanalysis at line 98
In fixsampleinfo at line 105
In ft_datatype_raw at line 148
In ft_checkdata at line 277
In ft_timelockanalysis at line 98
the call to "ft_selectdata" took 0 seconds
the call to "ft_selectdata" took 0 seconds
the call to "ft_timelockanalysis" took 0 seconds
Inverse solution - Minimum-norm estimation - from FEM
% The output avg.pow contains the activity of the source through time
load('leadfield_mri_fem.mat'); leadfield_mri_fem.label = avgCond2.label; load('headmodel_mri.mat');
cfg = []; cfg.method = 'mne'; cfg.grid = leadfield_mri_fem; cfg.headmodel = headmodel_mri;
cfg.mne.prewhiten = 'yes'; cfg.mne.lambda = 3; %scaling to the noise variance
cfg.mne.scalesourcecov = 'yes';
sourceCond1 = ft_sourceanalysis(cfg,avgCond1); sourceCond2= ft_sourceanalysis(cfg, avgCond2);
the input is timelock data with 24 channels and 375 timebins
Warning: use cfg.sourcemodel instead of cfg.grid
In ft_checkconfig at line 130
In ft_sourceanalysis at line 192
the call to "ft_selectdata" took 0 seconds
using precomputed leadfields
the call to "ft_selectdata" took 0 seconds
estimating current density distribution for repetition 1
using precomputed leadfields
computing the solution where the noise covariance is used for regularisation
prewhitening the leadfields using the noise covariance
scaling the source covariance
The input data are real-valued, assuming sensor time-series: Computing the dipole moments
the call to "ft_sourceanalysis" took 0 seconds
the input is timelock data with 24 channels and 375 timebins
Warning: use cfg.sourcemodel instead of cfg.grid
In ft_checkconfig at line 130
In ft_sourceanalysis at line 192
the call to "ft_selectdata" took 0 seconds
using precomputed leadfields
the call to "ft_selectdata" took 0 seconds
estimating current density distribution for repetition 1
using precomputed leadfields
computing the solution where the noise covariance is used for regularisation
prewhitening the leadfields using the noise covariance
scaling the source covariance
The input data are real-valued, assuming sensor time-series: Computing the dipole moments
the call to "ft_sourceanalysis" took 0 seconds
Inverse solution - Minimum-norm estimation - from BEM
% The output avg.pow contains the activity of the source through time
load('leadfield_mri_bem.mat'); leadfield_mri_bem.label = avgCond2.label; load('headmodel_BEM.mat');
cfg = []; cfg.method = 'mne'; cfg.grid = leadfield_mri_bem; cfg.headmodel = headmodel_BEM;
cfg.mne.prewhiten = 'yes'; cfg.mne.lambda = 3; %scaling to the noise variance
cfg.mne.scalesourcecov = 'yes';
sourceCond1 = ft_sourceanalysis(cfg,avgCond1); sourceCond2= ft_sourceanalysis(cfg, avgCond2);
the input is timelock data with 24 channels and 375 timebins
Warning: use cfg.sourcemodel instead of cfg.grid
In ft_checkconfig at line 130
In ft_sourceanalysis at line 192
the call to "ft_selectdata" took 0 seconds
using precomputed leadfields
the call to "ft_selectdata" took 0 seconds
estimating current density distribution for repetition 1
using precomputed leadfields
computing the solution where the noise covariance is used for regularisation
prewhitening the leadfields using the noise covariance
scaling the source covariance
The input data are real-valued, assuming sensor time-series: Computing the dipole moments
the call to "ft_sourceanalysis" took 0 seconds
the input is timelock data with 24 channels and 375 timebins
Warning: use cfg.sourcemodel instead of cfg.grid
In ft_checkconfig at line 130
In ft_sourceanalysis at line 192
the call to "ft_selectdata" took 0 seconds
using precomputed leadfields
the call to "ft_selectdata" took 0 seconds
estimating current density distribution for repetition 1
using precomputed leadfields
computing the solution where the noise covariance is used for regularisation
prewhitening the leadfields using the noise covariance
scaling the source covariance
The input data are real-valued, assuming sensor time-series: Computing the dipole moments
the call to "ft_sourceanalysis" took 0 seconds
References
Most of the codes and presented above are inspired by the tutorials available through the followoing links:
Data from Subject01 are available at: